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.
Linear Discriminant Analysis, or LDA for short, is a predictive modeling algorithm for multiclass classification. It can also be used as a dimensionality reduction technique, providing a projection of a training dataset that best separates the examples by their assigned class.
The ability to use Linear Discriminant Analysis for dimensionality reduction often surprises most practitioners.
In this tutorial, you will discover how to use LDA 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.
 LDA is a technique for multiclass classification that can be used to automatically perform dimensionality reduction.
 How to evaluate predictive models that use an LDA projection as input and make predictions with new raw data.
Let’s get started.
Tutorial Overview
This tutorial is divided into four parts; they are:
 Dimensionality Reduction
 Linear Discriminant Analysis
 LDA ScikitLearn API
 Worked Example of LDA for Dimensionality
Dimensionality Reduction
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.
Linear Discriminant Analysis
Linear Discriminant Analysis, or LDA, is a linear machine learning algorithm used for multiclass classification.
It should not be confused with “Latent Dirichlet Allocation” (LDA), which is also a dimensionality reduction technique for text documents.
Linear Discriminant Analysis seeks to best separate (or discriminate) the samples in the training dataset by their class value. Specifically, the model seeks to find a linear combination of input variables that achieves the maximum separation for samples between classes (class centroids or means) and the minimum separation of samples within each class.
… find the linear combination of the predictors such that the betweengroup variance was maximized relative to the withingroup variance. […] find the combination of the predictors that gave maximum separation between the centers of the data while at the same time minimizing the variation within each group of data.
— Page 289, Applied Predictive Modeling, 2013.
There are many ways to frame and solve LDA; for example, it is common to describe the LDA algorithm in terms of Bayes Theorem and conditional probabilities.
In practice, LDA for multiclass classification is typically implemented using the tools from linear algebra, and like PCA, uses matrix factorization at the core of the technique. As such, it is good practice to perhaps standardize the data prior to fitting an LDA model.
For more information on how LDA is calculated in detail, see the tutorial:
Now that we are familiar with dimensionality reduction and LDA, let’s look at how we can use this approach with the scikitlearn library.
LDA ScikitLearn API
We can use LDA to calculate a projection of a dataset and select a number of dimensions or components of the projection to use as input to a model.
The scikitlearn library provides the LinearDiscriminantAnalysis 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 = ... svd = LinearDiscriminantAnalysis() svd.fit(data) transformed = svd.transform(data) 
The outputs of the LDA can be used as input to train a model.
Perhaps the best approach is to use a Pipeline where the first step is the LDA transform and the next step is the learning algorithm that takes the transformed data as input.

... # define the pipeline steps = [(‘lda’, LinearDiscriminantAnalysis()), (‘m’, GaussianNB())] model = Pipeline(steps=steps) 
It can also be a good idea to standardize data prior to performing the LDA transform if the input variables have differing units or scales; for example:

... # define the pipeline steps = [(‘s’, StandardScaler()), (‘lda’, LinearDiscriminantAnalysis()), (‘m’, GaussianNB())] model = Pipeline(steps=steps) 
Now that we are familiar with the LDA API, let’s look at a worked example.
Worked Example of LDA for Dimensionality
First, we can use the make_classification() function to create a synthetic 10class 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, n_classes=10) # summarize the dataset print(X.shape, y.shape) 
Running the example creates the dataset and summarizes the shape of the input and output components.
Next, we can use dimensionality reduction on this dataset while fitting a naive Bayes model.
We will use a Pipeline where the first step performs the LDA transform and selects the five most important dimensions or components, then fits a Naive Bayes model on these features. We don’t need to standardize 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.
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# evaluate lda with naive bayes 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.discriminant_analysis import LinearDiscriminantAnalysis from sklearn.naive_bayes import GaussianNB # define dataset X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=7, n_classes=10) # define the pipeline steps = [(‘lda’, LinearDiscriminantAnalysis(n_components=5)), (‘m’, GaussianNB())] 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.
In this case, we can see that the LDA transform with naive bayes achieved a performance of about 31.4 percent.
How do we know that reducing 20 dimensions of input down to five is good or the best we can do?
We don’t; five 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.
LDA is limited in the number of components used in the dimensionality reduction to between the number of classes minus one, in this case, (10 – 1) or 9
The example below performs this experiment and summarizes the mean classification accuracy for each configuration.
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# compare lda number of components with naive bayes 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.discriminant_analysis import LinearDiscriminantAnalysis from sklearn.naive_bayes import GaussianNB 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, n_classes=10) return X, y
# get a list of models to evaluate def get_models(): models = dict() for i in range(1,10): steps = [(‘lda’, LinearDiscriminantAnalysis(n_components=i)), (‘m’, GaussianNB())] models[str(i)] = Pipeline(steps=steps) return models
# evaluate a give model using crossvalidation def evaluate_model(model): 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) 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.show() 
Running the example first reports the classification accuracy for each number of components or features selected.
We can 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.
The results suggest using the default of nine components achieves the best performance on this dataset, although with a gentle tradeoff as fewer dimensions are used.

>1 0.182 (0.032) >2 0.235 (0.036) >3 0.267 (0.038) >4 0.303 (0.037) >5 0.314 (0.049) >6 0.314 (0.040) >7 0.329 (0.042) >8 0.343 (0.045) >9 0.358 (0.056) 
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 nine.
We may choose to use an LDA transform and Naive Bayes 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 code below provides an example of fitting and using a final model with LDA transforms on new data.

# make predictions using lda with naive bayes from sklearn.datasets import make_classification from sklearn.pipeline import Pipeline from sklearn.discriminant_analysis import LinearDiscriminantAnalysis from sklearn.naive_bayes import GaussianNB # define dataset X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=7, n_classes=10) # define the model steps = [(‘lda’, LinearDiscriminantAnalysis(n_components=9)), (‘m’, GaussianNB())] model = Pipeline(steps=steps) # fit the model on the whole dataset model.fit(X, y) # make a single prediction row = [[2.3548775,–1.69674567,1.6193882,–1.19668862,–2.85422348,–2.00998376,16.56128782,2.57257575,9.93779782,0.43415008,6.08274911,2.12689336,1.70100279,3.32160983,13.02048541,–3.05034488,2.06346747,–3.33390362,2.45147541,–1.23455205]] 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 nine most important components from the LDA 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 naive bayes model in order to predict the class label.
Further Reading
This section provides more resources on the topic if you are looking to go deeper.
Tutorials
Books
APIs
Articles
Summary
In this tutorial, you discovered how to use LDA for dimensionality reduction when developing predictive models.
Specifically, you learned:
 Dimensionality reduction involves reducing the number of input variables or columns in modeling data.
 LDA is a technique for multiclass classification that can be used to automatically perform dimensionality reduction.
 How to evaluate predictive models that use an LDA projection as input and make predictions with new raw data.
Do you have any questions?
Ask your questions in the comments below and I will do my best to answer.