python-scikit-learnUsing QDA classifier example

from sklearn import datasets, discriminant_analysis, model_selection

X, y = datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)

model = discriminant_analysis.QuadraticDiscriminantAnalysis()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)ctrl + cgithub

`from sklearn import` import module from scikit-learn	`load_iris` loads Iris dataset
`model_selection.train_test_split` splits given `X` and `y` datasets to test (25% of values by default) and train (75% of values by default) subsets	`discriminant_analysis.QuadraticDiscriminantAnalysis(` creates QDA model
`.fit(` train transformation model	`.predict(` predict target variable based on given features dataset

Usage example

from sklearn import datasets, discriminant_analysis, model_selection

X, y = datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)

model = discriminant_analysis.QuadraticDiscriminantAnalysis()
model.fit(X_train, y_train)

print(model.score(X_test, y_test))

output

0.9736842105263158

Using LDA classifier example

More of Python Scikit Learn

See more codes...

python-scikit-learnUsing QDA classifier example

Usage example

output

Related

More of Python Scikit Learn