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Guide of Mlr 100 Form

hi everyone um i hope you're well um.thank you very much for.joining me today um if you.can't hear me or you don't understand.what i'm saying or if you've got any.questions.uh please drop me a line in the chat i'm.going to try and monitor that as i.go along so my name is.heben i'm the author of machine learning.with.r the tidiburs and mlr with manning.and i'm a life science researcher.working.at the francis crick institute in london.and today i'm going to show you how you.can.train tune and cross-validate.a support vector machine model in our.using the.mlr package so.support vector machines are models for.supervised machine learning so.performing machine learning tasks.where we have some ground truth in our.training data that we can.compare to and support vector machines.can be used for both classification.where we're trying to predict a.categorical outcome.and they can also be used to train um.regression models where we're trying to.predict.a continuous outcome and.the support vector machine algorithm.works by.trying to find a hyperplane.that extends through your feature space.that best separates the classes in a.classification task.um and here is a sort of.silly contrived example from my book.where we measure the amount of money.that you make your company say in a.given week.and you also measure the amount of time.that you spend playing video games.at your desk and every day you record.these two variables and you also record.whether your boss is in a good mood.or a bad mood with you um and so.we're trying to use a support vector.machine.algorithm to train a model that will.help you predict whether your boss is in.a good mood or a bad mood with you which.is a very important application of.machine learning.um and so you can see that in this.example we have.two classes this is an example of a.binary classification problem.and the two classes are um.very easily separable they're completely.distinct from one another.um and so actually there are um a number.of decision boundaries that we could use.to separate these two classes.but the support vector machine algorithm.tries to learn.the the decision boundary the hyper.plane um.that separates your classes.but which also maximizes the margin.around itself the region around itself.for which there are no or few cases.and the reason that it's important that.it does this is that.this is the the hyper plane or the.decision boundary.that is at least based on the training.data that is.most likely to generalize well to um.future data sets.and the support vector machine algorithm.is.very popular and very powerful and one.of the reasons for that is that.it's actually very good at learning um.complex.non-linear decision boundaries which.seems like a little bit of a.contradiction after i've just told you.that it specifically learns.hyperplanes which by definition are.linear.and the way that support vector machines.can learn.non-linear decision boundaries is that.they can take advantage of something.called the kernel trick.where they're able to add an additional.dimension.um to your feature space that helps.separate your classes.so here is an example where we have a.two-dimensional feature space.um and we have cases belonging to two.classes and you can see that.there is no linear hyperplane that would.do a particularly good job at separating.these two classes um but using a kernel.function.the support vector machine algorithm can.add a new.dimension um to your data set and you.can kind of think of this as kind of a.stretching or a.deforming of the feature space in a way.that helps separate the classes.and so in this example you can see that.we're kind of stretching up.the the feature space from this original.two-dimensional feature space into a.three-dimensional feature space.where now um the algorithm is able to.find a linear hyperplane.that actually does a good job of.separating these two classes.and if we project this hyperplane onto.the this kernel function um and take.where it cuts this kernel function.and and project this back on to our.original two-dimensional feature space.you can see that now we have a a complex.non-linear decision boundary that is.able to separate.our classes and so i'm going to show you.how.we can train a support vector machine.model.in ah.okay so um for those of you that are.used to.working with r and then asked to do um.you may know or you may find it useful.to know but if you push.ctrl shift and r uh you can enter a.section label so i'm going to start by.loading packages so i'm going to load.the.mlr package and.the tidyverse set of packages which are.a set of packages for um.manipulating cleaning and plotting um.your.your data my book covers tidyburst tools.in quite a bit of.depth so then we're going to.load a dataset and i'm going to load.the spam dataset.from the kernlab package.and this is a data set that contains.um one row per email.and every column is a different.metric or measurement made on that email.so for example.it could be the number of capital.letters whether there's a url present.the number of exclamation marks etc.um and this data set is a labeled data.set where some of the rows.are labeled as being spam so junk mail.builder is not desirable.um or as not spam.so genuine emails that we would be.interested in receiving.um it's not a.massive data set so if i check out um.the.dimensions um we've got four thousand.six hundred and one.rows so four thousand six hundred and.one emails.and uh 58 columns um that's not a.massive.data set but for the sake of speeding.things up today just so that you guys.aren't having to.watch my computer we're away for ages um.i'm going to.um downsample that just so we're working.with a smaller data set.and i'm also going to convert it into a.tibble so.um you you're probably used to working.with data frames.in r um the tibial is the.tidy burst reimagining of a data frame.um and it has some nice properties to it.and some of the most useful ones are.that it.uh prints your data in a nicer cleaner.way and i'll show you what i mean by.that um in a second.but we could continue using data frames.for the whole of this example it's just.my personal preference.okay so i'm going to create a new object.called.spam tib and i'm going to use the sample.n function to sample just.1000 rows randomly from the spam data.frame.and i'm going to pipe this so.this pipe operator takes the.returned value of the function on its.left and passes it as the first argument.to the function on its right and i'm.just going to.pass this into as typical to convert it.into a.table and now if i call this.spam tip then.you can see that we've got a typical.with a thousand rows and 58.columns and um you can see some nice.features of.how tables are printed here so it'll.only show.the number of columns that will fit in.your console.and the remaining columns are.listed below it only shows you the first.10 rows by default and also it tells.you the type of column.that each variable corresponds to so.whether it's double factor character.logical.etc.another useful function is the glimpse.function.which um kind of um turns the data.on its side and instead we get.kind of one row per column so this is a.a.nice way of quickly seeing.all of the columns that you have in your.data set and it shows the first several.values for any of your.your variables and if i come right to.the bottom you'll see that we have this.variable.at the end called type which is a factor.which has two levels either spam or.non-spam as our labeled data.um and actually it would be useful for.us to get an idea as to the proportions.um of those two categories that we have.so i can call.table on spam tib and.the type variable and i can see that in.this data set i have.596 non-spam emails and 404.spam emails.okay so in order for us to train a model.with mlr.we need to first create two things we.need to create.our task and our task is just.a definition of the data set that we're.working with.and if it's a supervised machine.learning task.uh it also includes a definition of what.our target variable.is is going to be so which variable we.want our model to.predict the second thing that we need is.to define a learner.and the learner in in mlr.is simply the definition of the.algorithm that we're going to use to.train the model.uh in addition to any additional options.that we might want to supply.um to that algorithm so uh.defines define task.and learner so my spam task.is going to be.so this is a classification task because.we are.trying to predict a discrete category.spam or not spam.and so the function to create a.classification task is make.classif task if this was a regression.task.and we were trying to predict a.continuous variable.then it would be make reg task.um if this was a survival task make a.serv task if it was a cluster task may.make plus tax task etc.so this is a classification task.and we first give the data.which is our spam tip table and.as this is a supervised machine learning.task we also have to provide.an argument um as the target and this is.the name of the column that we're trying.to predict so this in this case is the.type.column of course if this was an.unsupervised.machine learning task such as a.clustering task where there is no target.variable.we simply omit the target argument.because it doesn't make sense.to supply one.so when you create a task you'll get a.warning.um that basically just says that the.data that you've provided is not a pure.data frame.but is of class a typical data frame um.this still works.um it's just warning you because it.expected a data frame and it got a.tibble.instead but you can safely ignore this.warning.okay so now i'm going to create my.learner.i'm going to call my learner spm for.support vector machine.and to make a learner we use the.function.make learner and mlr.comes with a large number of machine.learning algorithms.already built in and you can define your.own.if the one that you want is not.implemented.and they follow a particular naming.scheme where.every learner has a prefix.and a suffix and the prefix just tells.you.whether it is a classification.clustering regression.or survival task etc um.and then the suffix is the the name of.the the algorithm or.is usually actually the name of the.function.from the package that the algorithm is.is derived from.and in between the prefix and the suffix.the name just has a period.so for example for today.the name of our learner is going to be.classif dot svm.because it's a classification task.and we're going to use the support.vector machine algorithm.um this might seem a little bit overkill.but as.a number of algorithms can be used for.both classification and regression.such as the svm algorithm it's important.to tell mlr.which variant you're you're intending to.use.um there's another an optional argument.um which uh i recommend which is.um predict dot type.um and i i recommend that you said this.to.um prob so the default for this argument.is um uh that.uh actually what is the what is the name.of the default make.response that's it okay so the default.is response.and what this means is that models that.you train.using this learner definition if the.predict type is set to response when you.use that model to make predictions.on new cases on unseen data.the model will simply spit out the most.probable class.for that data so if we were to pass.some new emails through the model that.we're about to train it would just say.spam not spam without giving you any.um quantifiable measure of how confident.the model is.that that email was spam or not and so.for models that support it or algorithms.that support it.i recommend setting the predict.type.argument equal to prob.so now when you make um.when you make predictions on.new data rather than just getting spam.not spam.the model will actually um.it'll return the estimated probability.that it thinks that that case belongs to.a particular class and this helps you.make better decisions because.um one email could be.given a probability of being spam of.0.9 um so very confident that you met a.spam and another could be given a.probability of.0.51 um and if all the model gives you.is.spam or not spam you don't have the kind.of information to help you make better.decisions so you may for example.want to treat those probabilities closer.to 0.5.slightly differently you may want to.manually annotate those cases for for.example so we're going to.set predictor type to prop.okay so the next thing that we're going.to do is.define our hyperparameter search space.so a hyperparameter is a setting.or an option or a variable that controls.how an algorithm learns a model but that.cannot be estimated directly from the.data.instead and the svm.algorithm has a number of hyper.parameters that we need to set.and one way to to set them would be to.for example pick values that seem.sensible to us or that have.worked well for similar problems in the.past.um but a more sensible way is actually.to.incorporate a tuning procedure um.during model training and allow the.model to.to filter out and find the best.performing combination of hyper.parameters.um automatically and so what we first.need to do in order to do that is to.define.our search space of hyper parameters.basically.uh set or a combination of possible.hyper parameter values.that we want to search over in order to.find the best combination.okay so um for any given algorithm you.can find.uh the available hyper parameters uh.hyper parameters for it.um by calling the get.param set function.i'm providing the name of the learner.if i expand this out so you can see that.um you get a data frame where every row.corresponds to a particular hyper.parameter.we get the type so whether it's uh it's.a discrete type of parameter or numeric.or.logical um the we get the default values.so for example uh the default of this.cost hyper parameter is one.um we get the constraints uh so the.the the upper and lower band bounds if.it has it so for example the cost.is uh constrained between zero and.infinity.um and whether that particular hyper.parameter is.tunable or not so the most important.hyper parameters.uh of the svm algorithm for us to tune.are the cost the.kernel degree.and gamma have i missed anything yet.no no those guys are the most important.so what do i mean by those.okay so i mentioned here that um support.vector machines.are kernel methods they can use kernel.functions to deform and stretch the.feature space.to help separate classes um and there.are a number of different kernel.functions that will deform the feature.space.in different ways and that may be useful.in different situations.um and so here are some examples of some.of the most.common um kernel functions um.and choosing.um or as the the kernel function.can't be directly estimated um from um.or our choice of kernel function can't.be directly estimated from the data.um the the kernel function that we use.to train the model.is a discrete type of parameter and so.we have to.during our hyperparameter tuning process.get the model training process to select.the best performing.kernel so we have the linear kernel.which is actually equivalent to no.kernel at all.we can use polynomial kernels which will.learn.the decision boundaries of.increasing flexible complexity so.as you increase the degree of polynomial.from second to third to fourth etc.and if we choose the polynomial kernel.then the degree of that kernel is also.an additional hyper parameter.that we would tune the radial basis.kernel function is actually the default.and this is kind of shown in in this.example here actually.and the radial basis kernel function is.very good at.um at learning.or deforming the feature space in such a.way but it helps.us to um form.decision boundaries between um clusters.of cases that may be surrounded by other.classes.for example or that are discontinuous.from each other as as in this example.um or another example of a kernel.function that is useful when you have.one class that is maybe bounded.by another class or other classes is the.sigmoid kernel function.the cost hyper parameter.tells the algorithm how bad it is.to have cases inside the margin so when.i gave you.this example earlier on and i mentioned.that.support vector machine algorithm learns.the.hyper plane that maximizes the margin.around itself.that's true in easy examples where.the cases are completely separated.but if you're working on problems as.easy as this then actually.support vector machine algorithm is.probably overkill for you um for more.complicated.real world examples it's very rare to.have margins that have absolutely.no cases inside them and so really when.we talk about the svm algorithm.most of the time we're actually talking.about soft svm.which means that actually it it does.allow.cases inside of its margin.and how bad it is to have cases inside.the margin depends upon the.cost hyperparameter so if cost is is low.then the model doesn't really mind that.much when there are lots of cases.inside the margin and.what this means is that if.your cost is is too low um.the algorithm may learn a decision.boundary which actually is quite.um insensitive to local changes um.near the uh decision boundary near the.um.the the separation between your two.classes.um and so if the cost is is too low then.you may end up.underfitting your your model.if cost is is high then.the model can consider it to be very bad.to have lots of cases inside the margin.um and so what you tend to have is the.algorithm.learning a decision boundary that very.harshly fits.um the the noise perhaps near the.boundary between the two classes and so.there's this trade-off between setting.your cost too low in which case you may.end up under fitting.and setting your cost too high which.case you may end up overfitting.there's a similar consideration for the.gamma.hyper parameter so the gamma.hyperparameter you can kind of think of.as how attention seeking.each individual case is in in the data.so if you imagine.um all the cases um kind of.vying for attention shouting you know.classify me correctly.then gamma hyperparameter is how loud.they all shout.um and so if your gamma is too.low then your um algorithm doesn't.really place too much.importance on learning a hyperplane.that's.correctly classifies all of the cases in.the data set and so if you set it too.low.again you may learn decision boundaries.that are too general.and underfit your data set if you set.gamma.too high then the opposite is true the.algorithm really puts a lot of emphasis.on learning decision boundaries.that will try to classify all the cases.correctly.and what you end up getting is decision.boundaries that are very granular and.very.overfit to the training set.okay so in order for us to.automatically tune um our hyper.parameters during training.the first thing we need to do is define.our search space to tell.the training algorithm uh what possible.combinations of these hyper parameters.we want.to select to to try and.validate um so i'm going to start by.defining a vector of the kernels that i.want to try.so we're going to try the polynomial.radial and sigmoid.kernels and notice that i've omitted the.linear kernel.um but that's just because we are also.going to.uh tune the degree hyperparameter the.degree of the polynomial.and i'm going to include the first.degree polynomial in that which of.course is just the.linear kernel.okay and so to find a vector of my.kernels and now i'm going to.define svm param space.and in order for us to define a hyper.parameter search space.we use the function.make param set.and in this function we simply provide a.definition.of the hyperparameter that we're going.to.tune and we define either a set of.plausible values or perhaps an upper or.a lower limit and.we simply separate each of these hyper.parameter definitions.by a comma within the function so the.first one i'm going to do.is i'm going to define our kernel.hyperparameter which is a.discrete hyperparameter it can only take.one of these.three values so the function to create a.discrete hyperparameter.is make discrete.param and the name of the hyper.parameter as we got.from this table is kernel.and its possible values are equal to.kernel this vector that i defined here.actually that's kernels.okay so that's my first type of.parameter the second one i'm going to.define.is the degree hyperparameter and this.will only really.uh matter as and when uh the search.process.selects polynomial um as the kernel and.otherwise this particular hyperparameter.has no effect on the radial.or the sigmoid kernels and so the.the the degree um i have a parameter is.uh it must be an integer um because it's.the.degree of the polynomial and i'm going.to define this to be between the values.of one.and three so i'm going to use the.function make.integer param this is the.degree and i'm going to find the.lower bound as one and the.upper as three.the next type of parameter i'm going to.define is cost which.is a numeric hyperparameter so the cost.can take on.you know floating point and decimal.values.and so i'm going to use the function.make.numeric param.this is cost and the lower e.is equal to five and the upper.is equal to 20. and you might be.wondering why i've selected these values.and it's partly because i've.run this example before and i know these.values work well.but um frankly when you're you're.training hyper parameters.start by setting the net relatively wide.and use.some of your knowledge as to perhaps.what you've tried um in in the past for.um similar um.problems start by casting your net wide.and.actually i probably won't show you today.but actually it is quite important to.plot your hyper parameter tuning process.to ensure.um that you are at um.a minimum um hopefully the global.minimum um in terms of the loss.during your your model training um but.um.you you want to make sure that for.example if you didn't continue going.higher or lower with a particular hyper.parameter value and that you wouldn't.get.better performance but for the cost.today.i'm going to bound it between 5 and 20..and then finally i'm going to define.the gamma hyperparameter so again make.numeric param gamma.and the oops.lower is equal to zero and the upper is.equal to.two okay so now i've got my definition.of.the hyper parameter search space i now.need to define actually.how this search face is going to be.explored or sampled.during model training.okay so when we're training the model um.we are basically going to.end up training the model um many many.times each time.using a different combination of um.hyper parameters.um and there are different ways that we.can.select combinations of of hyper.parameters to try.um and then to validate their.performance um.one common way is to use what's called a.grid search.which is to take every from the.definition of.of our hyper parameter search space take.every possible combination of all of.those hyper parameters.and tune a model sorry and train a model.using.that combination of hyper parameters and.then.evaluate the performance of all those.models and pick the one.combination that results in the best.performing model.and that is the only.search method that is guaranteed to find.the best performing combination of hyper.parameters.within your search space okay the.problem with it is that unless you have.a small number of um discrete.hyper parameters um then actually the.hyper parameter search space.blows up to a massive size.um and you could end up having to train.millions.of models and actually in this example.that i've defined here.we basically have an infinite number of.combinations of these hyper parameters.because i have.um numeric parameters in there that can.take on floating point and decimal.values.and so in this situation a grid search.is is really not appropriate.but let's say for example you only had.one.or two discrete or integer.hyper parameters then you may prefer a.grid search because it's.guaranteed to find the best performing.combination.within the bounds that you've defined as.your search space.a more pragmatic approach is to perform.a random search and for a random search.during model training we simply.randomly select a combination of.hyperparameters and we train a model.with that combination.and then when we train the next model we.again randomly select um another.combination of hyper parameters.um and we train a model um on that and.we do this for a fixed.number of iterations um.basically um whatever our computational.budget is.um and of all of those.sampled combinations up to the maximum.number of iterations.um we then find the combination that.performs the best.okay and of course this method is.not guaranteed to find the best.performing combination of hyper.parameters.in your search space but the.more iterations that you have the more.likely you are.um to come up with the best performing.combination.um and so this is what we are going to.use uh today.so i'm going to define an object called.rand search.and the function that i used to define.this is make.tune control random.and if i was performing a grid search.then this would be make tune control.grid there is another option.which is make tune control.irace which uses the iterated.f race algorithm for trying to find the.best combination of hyper parameters.and this is kind of a happy intermediate.uh.between the grid search uh which might.be impossible.and a random search so it is.um more likely to find the best.performing combination.um of hyper parameters than uh random.search.um but will still take longer.than a random search depending on the.number of iterations that that you.you you apply for a random search um but.for today we are just going to define.make control.um make tune control random and we need.to define a number of.the maximum number of iterations that we.want to perform and.again you should set this based on your.computational budget but.for today just for the sake of saving.some time.i'm going to set this to 20 iterations.okay so the next thing that i'm going to.do is define.how i'm going to evaluate.the performance of of um my model.of my model training process and how i'm.going to evaluate the performance.um actually of each of these models.trained on different combinations of.hyper parameters and i'm going to do.that with a process called.cross validation and more specifically.i'm going to use.a technique called nested cross.validation.okay so what is nested.cross-validation okay.so if you've done any sort of um machine.learning.in the past there we go sorry.so if you've done any sort of machine.learning in the past then.you will probably have come across.the whole paradigm of.training sets validation set and test.set.where in order to get an idea.of how your model will perform on future.unseen data.it's extremely important that you don't.evaluate its performance.on the data that you use to train in the.first place because models will almost.always perform better on the data that.you use to train them.than on new data and so um.commonly what people will do is that.they will split their data.into um a portion.that they'll call the training set and.these will be perhaps randomly.selected rows that the algorithm.has access to during training and.they'll train a model.on the training set um and then they'll.take that model.and they'll evaluate it on another.partition of the data.called the test set and the test set.arose from the data.that the algorithm did not see during.the model trading process.and they'll in a supervised machine.learning setting.they will get the model to make.predictions on the test set.and we'll compare those predictions with.the ground truth.and we'll report some kind of metric.that helps us evaluate.how well the model has performed one of.the simplest for classification.being the accuracy of the model what.proportion of cases did the model.correctly classify for example and.sometimes.what people will also do is they will um.have a third um set or partition of.their data set.that they will call the validation set.and what they'll do is they will.train their model using the training set.um perhaps using.um one combination of hyper parameters.um or some kind of settings or options.for their model training process they'll.train that model and their training set.and then they'll evaluate it on the.validation set and then they'll tweak.and they'll change those hyper.parameters.and train a model using the training set.and then evaluate it on the validation.set and keep doing that.training on the training set and.evaluating on the validation set.until they feel that they have a.combination of hyper parameters.that they think works the best gives the.best performance um.on the but the validation set and then.once they have that.they will then um.evaluate their model on the test set.which has not been seen by the algorithm.at any point during this model training.and tuning process.and then we'll use that then as a sort.of final metric for how.they believe that their model will.perform on on new data.and if you've got an algorithm that is.uh computationally intensive.or if you've got a situation where you.have a very large data set.um and so the performance metrics.that you get back are actually quite.robust and don't change um.from from time to time then actually the.this this paradigm of training set.validation set test sets um is very.robust.and it works well and many people do it.but if you have the computational.budget and or if.your the number of cases that you have.is quite small.um then i prefer to use nested.cross validation instead and so let me.just.go through it and explain in a little.bit of detail so hopefully it'll.make more sense so um in this example.today.um what we're going to do is take our.our data set and split it into three.folds.okay just like um k fold cross.validation.and we're going to reserve one fold as.our test set.and the other two folds we're going to.further split.into a training set and a test set.okay so we've got this kind of outer.partition.where we have a three-fold and we have.this inner partition using.just the training set which is actually.just simple holdout.cross-validation okay where we have one.portion as the training set.and one portion as the test set.um and then using this data we're gonna.using our random search procedure.we're going to randomly select a.combination of hyper parameters from the.search space that we defined.and we're going to train a model using.that combination.and then we're going to evaluate that.model on this test set.then we're going to do the same thing.again we're going to randomly select.another combination of hyper parameters.train the model on the training set.evaluate it on the test set.and then we're going to keep doing that.all the way for our 20.iterations that we we defined we were.going to only do.20 iterations of our random search.and at the end of this we've got 20.models all trained and evaluated.with 20 different combinations of hyper.parameters.and we're going to take the model that.gave us the best performance.on on set and we're going to pass that.winning combination of hyper parameters.um up to the outer loop.so this is the inner loop and this here.is the outer loop.then we're going to train a model use.oops.we're going to train a model using this.training set.using the that winning combination of.hyper parameters and then we're going to.evaluate it.on the outer test set and then we're.going to do that whole procedure.twice more where this time we're going.to use.this middle fold as the test set.and we're going to derive our inner loop.using.this block and this block okay and then.again.randomly select hyperparameter train a.model on the training set evaluate it on.the test set and do that for all.20 iterations pass the best.hyper parameter combination up to the.outer set and then we'll train a model.using those hyper parameters.using the the two training set folds.and evaluate it on the test set fold and.then we do that again.finally where now um this fold is.reserved as the outer.test set um and so what we end up with.is instead of just one um measure.of um model performance.on one test set we have three measures.okay and then what we what people will.usually do then is take the the.aggregate or the mean.um performance measure um across those.models.um and this has a couple of advantages.over the simple.training validation um test set paradigm.one of them is that we're not just.relying on.a single test set for.our evaluations because of course it.could be that.by sheer chance especially if your data.set is small that you picked a partition.of training and test set.um that meant that the.test sample was quite easy for the model.to classify.whereas in this case we're using all of.the data as as the test set.um at least once um the other.advantage is that although we're not.doing it in.in this example because we just have.simple holdout as our inner loop.but if you did something more.sophisticated as your inner loop say for.example.we did we also did three-fold.cross-validation um.on the inner loop um it actually means.that you have.a moving validation set so that you.you're not just.um reusing the same validation set over.and over and over again because of.course you.you can end up overfitting to your.validation set um although we're not.doing that um.today um of course the one big downside.of this is that you end up actually.having to train.your model um three times more.than if if we just did a simple.training validation and test set.okay so that might seem relatively.complicated.um and and in a way it is but thankfully.mlr.makes this uh very simple and very easy.um for.us to define so the first thing that we.need to do is define actually what our.inner and outer cross validation loops.are going to be.so in that example that i just gave you.our inner.cross validation strategy is simply.holdout.where we just reserve a proportion of.the rows as our.test set and the outer cross validation.loop is going to be.um three-fold cross-valid.excuse me three-fold cross-validation so.i'm going to define.my inner using the make.we sample desk functions for make.resampling description.and this is going to be simply hold out.and the split.corresponds to uh the proportion of rows.in the data that you're going to reserve.as the training set so i'm going to set.this as two-thirds.so that this ends up being two-thirds of.the data.used for training the model in the final.third used as the.held out portion for validating its.performance.that's my inner and then for my outer.again.oops make sample.description and this is going to be.um cv um which means k fold.um and the number of fighters is three.so this is threefold cross validation.for my outer loop.and there are some other um resampling.um strategies that we could have used so.we could have used.um so instead of hold out or cv here.we could have used l.o o for leave one out which is the.extreme of k.fold where you train a model using all.the cases and then.accept one and then evaluate it on that.one.and you do that for every single case in.the data set.we could have also used um and this is.particularly useful if you have a very.small data set.rep cv which is uh repeated cross.validation so for example.we could perform this three-fold.cross-validation once.then we could jumble up all the rows.and randomize them and then we could.perform.three-fold cross-validation again and we.could repeat that for.as many um iterations as as we wanted to.or we had budget for.um this is useful for small samples um.because actually you end up with many.more different combinations.of cases in the training and the test.set.and then finally we could also use sub.sampling.instead.okay so um in order for.the um the algorithm to.automatically tune the hyper parameters.uh for us.um we have to wrap together.the learner and the hyper parameter.tuning.process so.we're going to define.wrapper so i'm going to define an object.called.svm wrapper and use the function make.tune wrapper so what we're doing is.we're creating a new learner that is.kind of a.fusion of an existing learner and a.hyper parameter tuning process and so.what will happen then is that when we um.ask mlr.to train this learner um.the it will perform the hyper parameter.tuning process as part of the model.training process for us.hopefully it will make more sense in a.second if it doesn't already.so first we give it the.[Music].learner which is the svm learner that we.defined.here.we give it the resampling.strategy which is our inner cross.validation strategy.so this is what is going to be used to.cross-validate.each individual combination of hyper.parameters so remember.we are here and this is the.inner loop this is our holdout that.we're going to use to cross validate.each of these combinations.the par dot set is the um.hyper parameter search space that we.defined so this is.svm.paramspace that we defined here.and control.is the search method that we defined.as the random search which is here.and this is now my wrapped learner so.it's.a learner um but it also includes.hyper parameter tuning as part of the.the algorithm um okay so.now what i'm going to do is is run my.cross validation strategy to hopefully.give me an idea.of how this approach that i'm taking to.model.training will hopefully generalize to.future.data.okay so i'm going to define an object.cross validation.with tuning and to perform.cross-validation with mlr.you use the function called resample.and we give it the learner.and this learner is my wrapped learner.that we just created here.the task is a spam task.that we defined earlier to.tell us what our data and what our.target variable is.our resampling method is the outer cross.validation.loop which is our three-fold.cross-validation.and there's an additional optional.argument i'd recommend that you set.which is.models equals true.so the default for this argument is.false and.the resampling of the cross validation.procedure.will then simply return you the the.performance metrics.um and it discards the models it doesn't.save the models where actually sometimes.it's useful for you to.keep the model so that you can.interrogate them so i'm going to set.this to equal to.true and so if i run this.this is now performing my um.nested cross validation so at the moment.we are.on this first outer.fold and you'll see that.the algorithm is selecting a combination.of hyper parameters.training it on the tr on the the.training set and evaluating on the test.set.and then once it's done that 20 times.for a particular fold.um it will then pass up the winning.combination um.to the the outer loop we'll train a.model using the training data.using that combination of hyper.parameters and then evaluate it on the.test set and for classification tasks in.mlr the default performance metric.is the mmce or the mean.misclassification.error which is simply one minus.accuracy um and.if you've spent much time reading about.performance metrics.in machine learning you'll know that.actually accuracy is often.not a good measure of model performance.but for today.as our classes are relatively balanced.um.it's fine for for our purposes today and.so you can see now that it tells me that.the.aggregated result is an mmce.so a mean misclassification error mean.of um 0.0889842.so if i take this and i do one minus.basically we have an accuracy of 91.which isn't bad um for a first go um.and so this is the the mean um of the.mmce values for each of these three.folds of our outer cross validation.loop.and if i want to.if i call this object that we just saved.and extract the measures.test.then you can see i can get the.individual mmce values.for each of those folds of my outer.cross validation.loop okay so.um 91 isn't fantastic but let's pretend.for.um argument's sake today that i'm happy.uh with that level of performance and so.i.am going to train a final model using.all of my data.um using this same approach that i've.just taken here now.because once you cross validate your.your model to your model um.training process it's important for you.to then train your model using.all of the data available to you um.because models trained with more data.will have.less bias um.so.train model using.all data.so again i'm going to tune the hyper.parameters.um because um when we cross-validated.the model training process.we we weren't cross-validating a single.model we were cross-validating the.the process of of training the model in.in using that particular approach.um and so i'm going to tune the hyper.parameters again but this time using all.the data.available to me and so i'm going to.define an object called.tuned svm.i'm going to use the function tune.params.um that will actually just um.it is a function purely for the tuning.process so this will.return the optimal combination of hyper.parameters that i can then use to train.a model using all of the data available.to me.so again the learner is equal to svm.my task is.spam task.the resampling because of course you you.have to have some kind of way of.splitting your data in order to.evaluate the performance of each hyper.parameter so i'm just going to use my.inner cross validation strategy which is.simply holdout my.pass set again is my.parameter space that i defined.and the control groups.is my rand search.okay so then when i run this.it's now going to um 20 times.um select a random combination of hyper.parameters.and train a model using that combination.on the training set and evaluate it on.the.held out test set.and then at the end it tells me that the.result is that the.best performing kernel was a polynomial.of degree one.so actually just the linear kernel no.kernel at all.um with a cost of of 20 uh gamma of 1.18.and this is the particular um mmce that.we got.um but i won't really pay too much.attention to that one thing that should.be.a slight red flag is uh that the best.performing.cost parameter was 20. um.and if i look back up here where i.defined my hyper parameter space 20 was.the upper limit for this parameter.um and so this would make me worry that.actually i've.not defined my hyperparameter search.space correctly and actually perhaps i.should have included larger values.though what i would usually do is spend.time properly plotting.um the uh my my.performance metric against uh all of the.values.of the hyper parameters and see whether.actually even at 20.um my mean misclassification error was.still decreasing for example.and then adjust my parameter space.accordingly but for now i'll i'll.continue anyway.um and so actually if i print out.this then again you can see i get this.this this.so this that we performed was simply the.tuning process and all this returns.is the best combination of hyper.parameters um.during that random search that we um uh.we we undertook and so now what i'm.going to do is.train a model using all of the data.using this combination of.hyper parameters and so to do that.i'm going to define an object called.tuned svm.and use the function set hyper.parse and i'm going to use the svm.learner and.i want it to use the parameter values.that we just tuned.so this is tuned svm.powers x which.is simply a list of.the optimal values.well this isn't actually 20 okay it was.rounded but still.so what this will do is create a new.learner um by simply.taking our svm learner um and.uh assigning it this combination of.hyperparameter values.and this is now a new learner that we.can train.and to train.we simply use the train function and we.give it the.learner which is our.tuned svm.and the task.and this is now training the model using.all of the data and using.this combination of hyper parameters and.this will take a couple of seconds.um so that's our model trained.and so now at this point our model is.available to us uh to make predictions.on new data um i don't have any new data.prepared so actually i'm going to be.lazy and just make predictions on the.training data.which of course you would never normally.do but let's just imagine for example.that this is.some fresh data so we use the predict.function and we give the name.of our model.and the new data argument is where we.supply the.um new data that we're making.predictions on and this.data should have all of the columns um.that were.used to train the model.so if i do this then.we get the prediction for a thousand.observations and.um as the data that we passed to.the predict function already has the.column type which is what we were trying.to predict.um it actually tells us the uh the truth.um so it tells us whether it was uh spam.or non-spam.and it tells us the uh response from the.model um so whether the model is.predicting spam.or non-spam and remember that.way back um when we were defining our.learner i said it was useful to set the.predict.dot type argument to prob um and.we can see that that's paid off here.because basically now instead of.just getting just getting the.the response we actually get a.probability or a pseudo probability.of that particular case being in each of.our classes so the probability of being.spam and not spam and.there aren't really any ambiguous.examples here but that's useful because.um for example if you had an email.classified as.spam that had only you know a 0.51.probability of being spam.then you might be a little bit more.suspicious about those predictions.and you you may want to handle them.yourself.okay so.that is all i wanted to to cover today.so.let me just summarize what we've done.so we defined.um well we we loaded our data set that.contained one row per email and.one measurement on that email.per column and the data set was labeled.with whether each email was spam or not.spam as a ground truth.um and we used the support vector.machine algorithm to train a model to.try to predict.whether an email would be spam or not.the first thing that we did was to.define.a task which is a combination of the.data.and in a supervised setting which.variable.is our target variable that we're trying.to predict and we also defined a learner.which is simply a definition of which.algorithm.we're going to use um and that these.have a.naming scheme that follows a prefix and.a suffix um.to tell us what algorithm is available.to you to us.we defined our hyper parameter search.space.we defined our search procedure which.was random search.and then we defined our nested cross.validation.procedure um and then we cross validated.our.model training procedure to get an idea.as to.how um models trained using this.procedure.would generalize to future data and then.once we convinced ourselves that we were.happy with how our model was going to.perform we.tuned the hyper parameters once again.but this time using all of the data.and then we trained the model using all.of the data using that tuned set of.hyper parameters and we made some.predictions um using that model.so that's what i wanted to cover if.you're still with me.thank you very much um i am happy to.hang around for a little bit if you have.any questions um or if you can't think.of any questions now then.please feel free to add me and tweet me.on twitter um my handle is hrj21 i'll.just actually.um pop that in.the chat um i hang around for a couple.of seconds because in my.the last time i did this i stopped my.stream and there seemed to be a lag so.it cut me off halfway while i was.speaking so i'll.i'll hover around for a second but i.hope that.you guys have a nice rest of your day.thank you very much.

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Mlr 100 Form FAQs

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Do military members have to pay any fee for leave or fiancee forms?

First off there are no fees for leaves or requests for leave in any branch of the United States military. Second there is no such thing as a fiancée form in the U.S. military. There is however a form for applying for a fiancée visa (K-1 Visa)that is available from the Immigration and Customs Service (Fiancé(e) Visas ) which would be processed by the U.S. State Department at a U.S. Consulate or Embassy overseas. However these fiancée visas are for foreigners wishing to enter the United States for the purpose of marriage and are valid for 90 days. They have nothing to do with the military and are Continue Reading

How can I fill out Google's intern host matching form to optimize my chances of receiving a match?

I was selected for a summer internship 2016. I tried to be very open while filling the preference form: I choose many products as my favorite products and I said I'm open about the team I want to join. I even was very open in the location and start date to get host matching interviews (I negotiated the start date in the interview until both me and my host were happy.) You could ask your recruiter to review your form (there are very cool and could help you a lot since they have a bigger experience). Do a search on the potential team. Before the interviews, try to find smart question that you are Continue Reading

How do I fill out the form of DU CIC? I couldn't find the link to fill out the form.

Just register on the admission portal and during registration you will get an option for the entrance based course. Just register there. There is no separate form for DU CIC.

How do you know if you need to fill out a 1099 form?

It can also be that he used the wrong form and will still be deducting taxes as he should be. Using the wrong form and doing the right thing isnt exactly a federal offense

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While I did not study physics this is something that relates to my field as well. One thing to remember is the scope of the field which you are talking about. With physics it might seem narrower than History or Archaeology but I suspect that when you boil it down it isn’t. It would be impossible to cover everything in a subject even going all the way through to gaining a doctorate. The answer you got and posted up is very accurate and extremely good advice. What a lot of it boils down to in education (especially nowadays) is not so much teaching specific facts but teaching themes and how to find Continue Reading

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