Quadratic discriminant analysis is a method you can use when you have a set of predictor variables and you’d like to classify a response variable into two or more classes. It is considered to be the non-linear equivalent to linear discriminant analysis.
This tutorial provides a step-by-step example of how to perform linear discriminant analysis in Python.
Step 1: Load Necessary Libraries
First, we’ll load the necessary functions and libraries for this example:
from sklearn.model_selection import train_test_split from sklearn.model_selection import RepeatedStratifiedKFold from sklearn.model_selection import cross_val_score from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis from sklearn import datasets import matplotlib.pyplot as plt import pandas as pd import numpy as np
Step 2: Load the Data
For this example, we’ll use the iris dataset from the sklearn library. The following code shows how to load this dataset and convert it to a pandas DataFrame to make it easy to work with:
#load iris dataset iris = datasets.load_iris() #convert dataset to pandas DataFrame df = pd.DataFrame(data = np.c_[iris['data'], iris['target']], columns = iris['feature_names'] + ['target']) df['species'] = pd.Categorical.from_codes(iris.target, iris.target_names) df.columns = ['s_length', 's_width', 'p_length', 'p_width', 'target', 'species'] #view first six rows of DataFrame df.head() s_length s_width p_length p_width target species 0 5.1 3.5 1.4 0.2 0.0 setosa 1 4.9 3.0 1.4 0.2 0.0 setosa 2 4.7 3.2 1.3 0.2 0.0 setosa 3 4.6 3.1 1.5 0.2 0.0 setosa 4 5.0 3.6 1.4 0.2 0.0 setosa #find how many total observations are in dataset len(df.index) 150
We can see that the dataset contains 150 total observations.
For this example we’ll build a linear discriminant analysis model to classify which species a given flower belongs to.
We’ll use the following predictor variables in the model:
- Sepal length
- Sepal width
- Petal length
- Petal width
And we’ll use them to predict the response variable Species, which takes on the following three potential classes:
Step 3: Fit the QDA Model
Next, we’ll fit the QDA model to our data using the QuadraticDiscriminantAnalsyis function from sklearn:
#define predictor and response variables X = df[['s_length', 's_width', 'p_length', 'p_width']] y = df['species'] #Fit the QDA model model = QuadraticDiscriminantAnalysis() model.fit(X, y)
Step 4: Use the Model to Make Predictions
Once we’ve fit the model using our data, we can evaluate how well the model performed by using repeated stratified k-fold cross validation.
For this example, we’ll use 10 folds and 3 repeats:
#Define method to evaluate model cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1) #evaluate model scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1) print(np.mean(scores)) 0.97333333333334
We can see that the model performed a mean accuracy of 97.33%.
We can also use the model to predict which class a new flower belongs to, based on input values:
#define new observation new = [5, 3, 1, .4] #predict which class the new observation belongs to model.predict([new]) array(['setosa'], dtype='<U10')
We can see that the model predicts this new observation to belong to the species called setosa.
You can find the complete Python code used in this tutorial here.