This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.
Random Forests
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來自 University of Michigan 的課程
Applied Machine Learning in Python
2900 個評分
從本節課中
Module 4: Supervised Machine Learning - Part 2
This module covers more advanced supervised learning methods that include ensembles of trees (random forests, gradient boosted trees), and neural networks (with an optional summary on deep learning). You will also learn about the critical problem of data leakage in machine learning and how to detect and avoid it.
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Kevyn Collins-ThompsonAssociate Professor
School of Information
A widely used and effective method in machine learning
involves creating learning models known as ensembles.
An ensemble takes multiple individual learning models and combines them to
produce an aggregate model that is more
powerful than any of its individual learning models alone.
Why are ensembles effective?
Well, one reason is that if we have different learning models,
although each of them might perform well individually,
they'll tend to make different kinds of mistakes on the data set.
And typically, this happens because
each individual model might overfit to a different part of the data.
By combining different individual models into an ensemble,
we can average out their individual mistakes to reduce the risk
of overfitting while maintaining strong prediction performance.
Random forests are an example of the ensemble idea applied to decision trees.
Random forests are widely used in practice and
achieve very good results on a wide variety of problems.
They can be used as classifiers via the sklearn RandomForestClassifier class
or for regression using
the RandomForestRegressor class both in the sklearn ensemble module.
As we saw earlier, one disadvantage of using
a single decision tree was
that decision trees tend to be prone to overfitting the training data.
As its name would suggest,
a random forest creates lots of individual decision trees on a training set,
often on the order of tens or hundreds of trees.
The idea is that each of the individual trees in
a random forest should do reasonably well at predicting
the target values in the training set but should also be
constructed to be different in some way from the other trees in the forest.
Again, as the name would suggest this difference is accomplished by
introducing random variation into the process of building each decision tree.
This random variation during tree building happens in two ways.
First, the data used to build each tree is selected randomly and second,
the features chosen in each split tests are also randomly selected.
To create a random forest model you first decide on how many trees to build.
This is set using the n_estimated parameter for
both RandomForestClassifier and RandomForestRegressor.
Each tree were built from
a different random sample of the data called the bootstrap sample.
Bootstrap samples are commonly used in statistics and machine learning.
If your training set has N instances or samples in total,
a bootstrap sample of size N is created by just repeatedly
picking one of the N dataset rows at random with replacement,
that is, allowing for the possibility of picking the same row again at each selection.
You repeat this random selection process N times.
The resulting bootstrap sample has N rows just like
the original training set but with possibly some rows from the original
dataset missing and others occurring multiple times just
due to the nature of the random selection with replacement.
When building a decision tree for a random forest,
the process is almost the same as for
a standard decision tree but with one important difference.
When picking the best split for a node,
instead of finding the best split across all possible features,
a random subset of features is chosen and
the best split is found within that smaller subset of features.
The number of features in the subset that are randomly considered at
each stage is controlled by the max_features parameter.
This randomness in selecting the bootstrap sample
to train an individual tree in a forest ensemble,
combined with the fact that splitting a node in the tree is
restricted to random subsets of the features of the split,
virtually guarantees that all of
the decision trees and the random forest will be different.
The random forest model is quite sensitive to the max_features parameter.
Max_Features is set to one,
the random forest is limited to performing a split on the single feature that was
selected randomly instead of being able to take the best split over several variables.
This means the trees in the forest will likely be very different from each other and
possibly with many levels in order to produce a good fit to the data.
On the other hand if Max_features is high,
close to the total number of features that each instance has,
the trees in the forest will tend to be similar and probably will require
fewer levels to fit the data using the most informative features.
Once a random forest model is trained,
it predicts the target value for new instances by
first making a prediction for every tree in the random forest.
For regression tasks the overall prediction is
then typically the mean of the individual tree predictions.
For classification the overall prediction is based on a weighted vote.
Each tree gives a probability for
each possible target class label then the probabilities for
each class are averaged across all the trees and
the class with the highest probability is the final predicted class.
Here's an example of learning a random forest of
the example fruit dataset using two features, height and width.
Here we're showing the training data plotted in terms of two feature values with
height on the x axis and width on the y axis.
As usual, there are four categories of fruit to be predicted.
Because the number of features is restricted to just two in this very simple example,
the randomness in creating the tree ensemble is
coming mostly from the bootstrap sampling of the training data.
You can see that the decision boundaries
overall have the box like shape that we associate with
decision trees but with
some additional detail variation to accommodate
specific local changes in the training data.
Overall, you can get an impression of
the increased complexity of this random forest model in capturing
both the global and local patterns in
the training data compared to the single decision tree model we saw earlier.
Let's take a look at the notebook code that created
and visualized this random forest on the fruit dataset.
This code also plots the decision boundaries for the other five possible feature pairs.
Again, to use the RandomForestClassifier we import
the random forest classifier class from the sklearn ensemble library.
After doing the usual train test split and setting up the pipe plot figure for plotting,
we iterate through pairs of feature columns in the dataset.
For each pair of features we call the fit method on
that subset of the training data X using the labels y.
We then use the utility function plot class regions for classifier that's available in
the shared module for this course to
visualize the training data and the random forest decision boundaries.
Let's apply random forest to a larger dataset with more features.
For comparison with other supervised learning methods,
we use the breast cancer dataset again.
We create a new random forest classifier and since there are about 30 features,
we'll set max_features to eight to give
a diverse set of trees that also fit the data reasonably well.
We can see that random forest with no feature scaling or
extensive parameter tuning achieve
very good test set performance on this dataset, in fact,
it's as good or better than all the other supervised methods we've seen so far including
current life support vector machines and
neural networks that require more careful tuning.
Notice that we did not have to perform scaling or other
pre-processing as we did with a number of other supervised learning methods.
This is one advantage of using random forests.
Also note that we passed in a fixed value for
the random state parameter in order to make the results reproducible.
If we didn't set the random state parameter,
the model would likely be different each time due
to the randomized nature of the random forest algorithm.
So, on the positive side,
random forest are widely used because they're very powerful.
They give excellent prediction performance on a wide variety of problems
and they don't require careful scaling of the feature data or extensive parameter tuning.
And even though building many different trees
requires a corresponding increase in computation,
building random forests is easily paralyzed across multiple CPU's.
On the negative side while
random forests do inherit many of the benefits of decision trees,
one big difference is that random forest models can
be very difficult for people to interpret
making it difficult to see the predictive structure of
the features or to know why a particular prediction was made.
In addition, random forests are not a good choice for tasks that have
very high dimensional sparse features like text classification,
where linear models can provide efficient training and fast accurate prediction.
So to recap, here are some of
the key parameters that you'll need for using random forests.
N_estimators sets the number of trees to use.
The default value for n_estimators is 10 and increasing this number for
larger data sets is almost certainly a good idea
since ensembles that can average over more trees will reduce overfitting.
Just bear in mind that increasing the number of
trees in the model will also increase the computational cost of training.
You'll use more time and more memory.
So in practice you'll want to choose the parameters that
make best use of the resources available on your system.
As we saw earlier,
the max_features parameter has a strong effect on performance.
It has a large influence on how diverse the random trees in the forest are.
Typically, the default setting of max features,
which for classification is the square root of the total number of
features and for regression is the log base two of the total number of features,
works quite well in practice although explicitly adjusting max_features may give you
some additional performance gain with
smaller values of max features tending to reduce overfitting.
The max depth parameter controls the depth of each tree in the ensemble.
The default setting for this is none, in other words,
the nodes in a tree will continue to be split until all leaves contain
the same class or have fewer samples than the minimum sample split parameter value,
which is two by default.
Most systems now have a multi-core processor and so you can use the end jobs
parameter to tell the random forest algorithm
how many cores to use in parallel to train the model.
Generally, you can expect something close to a linear speed up.
So, for example, if you have four cores,
the training will be four times as fast as if you just used one.
If you set end jobs to negative one it will use all the cores on your system and
setting end jobs to a number that's more than
the number of cores on your system won't have any additional effect.
Finally, given the random nature of random forests,
if you want reproducible results it's especially
important to choose a fixed setting for the random state parameter.
In the examples we've shown here we typically set
random state to zero but any fixed number will work just as well.
