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Reducing the number of input variables for a predictive model is referred to as dimensionality reduction.
Fewer input variables can result in a simpler predictive model that may have better performance when making predictions on new data.
Perhaps the most popular technique for dimensionality reduction in machine learning is Principal Component Analysis, or PCA for short. This is a technique that comes from the field of linear algebra and can be used as a data preparation technique to create a projection of a dataset prior to fitting a model.
In this tutorial, you will discover how to use PCA for dimensionality reduction when developing predictive models.
After completing this tutorial, you will know:
 Dimensionality reduction involves reducing the number of input variables or columns in modeling data.
 PCA is a technique from linear algebra that can be used to automatically perform dimensionality reduction.
 How to evaluate predictive models that use a PCA projection as input and make predictions with new raw data.
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Tutorial Overview
This tutorial is divided into three parts; they are:
 Dimensionality Reduction and PCA
 PCA ScikitLearn API
 Worked Example of PCA for Dimensionality Reduction
Dimensionality Reduction and PCA
Dimensionality reduction refers to reducing the number of input variables for a dataset.
If your data is represented using rows and columns, such as in a spreadsheet, then the input variables are the columns that are fed as input to a model to predict the target variable. Input variables are also called features.
We can consider the columns of data representing dimensions on an ndimensional feature space and the rows of data as points in that space. This is a useful geometric interpretation of a dataset.
In a dataset with k numeric attributes, you can visualize the data as a cloud of points in kdimensional space …
— Page 305, Data Mining: Practical Machine Learning Tools and Techniques, 4th edition, 2016.
Having a large number of dimensions in the feature space can mean that the volume of that space is very large, and in turn, the points that we have in that space (rows of data) often represent a small and nonrepresentative sample.
This can dramatically impact the performance of machine learning algorithms fit on data with many input features, generally referred to as the “curse of dimensionality.”
Therefore, it is often desirable to reduce the number of input features. This reduces the number of dimensions of the feature space, hence the name “dimensionality reduction.”
A popular approach to dimensionality reduction is to use techniques from the field of linear algebra. This is often called “feature projection” and the algorithms used are referred to as “projection methods.”
Projection methods seek to reduce the number of dimensions in the feature space whilst also preserving the most important structure or relationships between the variables observed in the data.
When dealing with high dimensional data, it is often useful to reduce the dimensionality by projecting the data to a lower dimensional subspace which captures the “essence” of the data. This is called dimensionality reduction.
— Page 11, Machine Learning: A Probabilistic Perspective, 2012.
The resulting dataset, the projection, can then be used as input to train a machine learning model.
In essence, the original features no longer exist and new features are constructed from the available data that are not directly comparable to the original data, e.g. don’t have column names.
Any new data that is fed to the model in the future when making predictions, such as test dataset and new datasets, must also be projected using the same technique.
Principal Component Analysis, or PCA, might be the most popular technique for dimensionality reduction.
The most common approach to dimensionality reduction is called principal components analysis or PCA.
— Page 11, Machine Learning: A Probabilistic Perspective, 2012.
It can be thought of as a projection method where data with mcolumns (features) is projected into a subspace with m or fewer columns, whilst retaining the essence of the original data.
The PCA method can be described and implemented using the tools of linear algebra, specifically a matrix decomposition like an Eigendecomposition or SVD.
PCA can be defined as the orthogonal projection of the data onto a lower dimensional linear space, known as the principal subspace, such that the variance of the projected data is maximized
— Page 561, Pattern Recognition and Machine Learning, 2006.
For more information on how PCA is calculated in detail, see the tutorial:
Now that we are familiar with PCA for dimensionality reduction, let’s look at how we can use this approach with the scikitlearn library.
PCA ScikitLearn API
We can use PCA to calculate a projection of a dataset and select a number of dimensions or principal components of the projection to use as input to a model.
The scikitlearn library provides the PCA class that can be fit on a dataset and used to transform a training dataset and any additional dataset in the future.
For example:
... data = ... # define transform pca = PCA() # prepare transform on dataset pca.fit(data) # apply transform to dataset transformed = pca.transform(data)
The outputs of the PCA can be used as input to train a model.
Perhaps the best approach is to use a Pipeline where the first step is the PCA transform and the next step is the learning algorithm that takes the transformed data as input.
... # define the pipeline steps = [('pca', PCA()), ('m', LogisticRegression())] model = Pipeline(steps=steps)
It can also be a good idea to normalize data prior to performing the PCA transform if the input variables have differing units or scales; for example:
... # define the pipeline steps = [('norm', MinMaxScaler()), ('pca', PCA()), ('m', LogisticRegression())] model = Pipeline(steps=steps)
Now that we are familiar with the API, let’s look at a worked example.
Worked Example of PCA for Dimensionality Reduction
First, we can use the make_classification() function to create a synthetic binary classification problem with 1,000 examples and 20 input features, 15 inputs of which are meaningful.
The complete example is listed below.
# test classification dataset from sklearn.datasets import make_classification # define dataset X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=7) # summarize the dataset print(X.shape, y.shape)
Running the example creates the dataset and summarizes the shape of the input and output components.
(1000, 20) (1000,)
Next, we can use dimensionality reduction on this dataset while fitting a logistic regression model.
We will use a Pipeline where the first step performs the PCA transform and selects the 10 most important dimensions or components, then fits a logistic regression model on these features. We don’t need to normalize the variables on this dataset, as all variables have the same scale by design.
The pipeline will be evaluated using repeated stratified crossvalidation with three repeats and 10 folds per repeat. Performance is presented as the mean classification accuracy.
The complete example is listed below.
# evaluate pca with logistic regression algorithm for classification from numpy import mean from numpy import std from sklearn.datasets import make_classification from sklearn.model_selection import cross_val_score from sklearn.model_selection import RepeatedStratifiedKFold from sklearn.pipeline import Pipeline from sklearn.decomposition import PCA from sklearn.linear_model import LogisticRegression # define dataset X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=7) # define the pipeline steps = [('pca', PCA(n_components=10)), ('m', LogisticRegression())] model = Pipeline(steps=steps) # evaluate model cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1) n_scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=1, error_score='raise') # report performance print('Accuracy: %.3f (%.3f)' % (mean(n_scores), std(n_scores)))
Running the example evaluates the model and reports the classification accuracy.
Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.
In this case, we can see that the PCA transform with logistic regression achieved a performance of about 81.8 percent.
Accuracy: 0.816 (0.034)
How do we know that reducing 20 dimensions of input down to 10 is good or the best we can do?
We don’t; 10 was an arbitrary choice.
A better approach is to evaluate the same transform and model with different numbers of input features and choose the number of features (amount of dimensionality reduction) that results in the best average performance.
The example below performs this experiment and summarizes the mean classification accuracy for each configuration.
# compare pca number of components with logistic regression algorithm for classification from numpy import mean from numpy import std from sklearn.datasets import make_classification from sklearn.model_selection import cross_val_score from sklearn.model_selection import RepeatedStratifiedKFold from sklearn.pipeline import Pipeline from sklearn.decomposition import PCA from sklearn.linear_model import LogisticRegression from matplotlib import pyplot # get the dataset def get_dataset(): X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=7) return X, y # get a list of models to evaluate def get_models(): models = dict() for i in range(1,21): steps = [('pca', PCA(n_components=i)), ('m', LogisticRegression())] models[str(i)] = Pipeline(steps=steps) return models # evaluate a given model using crossvalidation def evaluate_model(model, X, y): cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1) scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=1, error_score='raise') return scores # define dataset X, y = get_dataset() # get the models to evaluate models = get_models() # evaluate the models and store results results, names = list(), list() for name, model in models.items(): scores = evaluate_model(model, X, y) results.append(scores) names.append(name) print('>%s %.3f (%.3f)' % (name, mean(scores), std(scores))) # plot model performance for comparison pyplot.boxplot(results, labels=names, showmeans=True) pyplot.xticks(rotation=45) pyplot.show()
Running the example first reports the classification accuracy for each number of components or features selected.
Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.
We see a general trend of increased performance as the number of dimensions is increased. On this dataset, the results suggest a tradeoff in the number of dimensions vs. the classification accuracy of the model.
Interestingly, we don’t see any improvement beyond 15 components. This matches our definition of the problem where only the first 15 components contain information about the class and the remaining five are redundant.
>1 0.542 (0.048) >2 0.713 (0.048) >3 0.720 (0.053) >4 0.723 (0.051) >5 0.725 (0.052) >6 0.730 (0.046) >7 0.805 (0.036) >8 0.800 (0.037) >9 0.814 (0.036) >10 0.816 (0.034) >11 0.819 (0.035) >12 0.819 (0.038) >13 0.819 (0.035) >14 0.853 (0.029) >15 0.865 (0.027) >16 0.865 (0.027) >17 0.865 (0.027) >18 0.865 (0.027) >19 0.865 (0.027) >20 0.865 (0.027)
A box and whisker plot is created for the distribution of accuracy scores for each configured number of dimensions.
We can see the trend of increasing classification accuracy with the number of components, with a limit at 15.
We may choose to use a PCA transform and logistic regression model combination as our final model.
This involves fitting the Pipeline on all available data and using the pipeline to make predictions on new data. Importantly, the same transform must be performed on this new data, which is handled automatically via the Pipeline.
The example below provides an example of fitting and using a final model with PCA transforms on new data.
# make predictions using pca with logistic regression from sklearn.datasets import make_classification from sklearn.pipeline import Pipeline from sklearn.decomposition import PCA from sklearn.linear_model import LogisticRegression # define dataset X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=7) # define the model steps = [('pca', PCA(n_components=15)), ('m', LogisticRegression())] model = Pipeline(steps=steps) # fit the model on the whole dataset model.fit(X, y) # make a single prediction row = [[0.2929949,4.21223056,1.288332,2.17849815,0.64527665,2.58097719,0.28422388,7.1827928,1.91211104,2.73729512,0.81395695,3.96973717,2.66939799,3.34692332,4.19791821,0.99990998,0.30201875,4.43170633,2.82646737,0.44916808]] yhat = model.predict(row) print('Predicted Class: %d' % yhat[0])
Running the example fits the Pipeline on all available data and makes a prediction on new data.
Here, the transform uses the 15 most important components from the PCA transform, as we found from testing above.
A new row of data with 20 columns is provided and is automatically transformed to 15 components and fed to the logistic regression model in order to predict the class label.
Predicted Class: 1
Summary
In this tutorial, you discovered how to use PCA for dimensionality reduction when developing predictive models.
Specifically, you learned:
 Dimensionality reduction involves reducing the number of input variables or columns in modeling data.
 PCA is a technique from linear algebra that can be used to automatically perform dimensionality reduction.
 How to evaluate predictive models that use a PCA projection as input and make predictions with new raw data.
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