The Ridge Classifier, based on Ridge regression method, converts the label data into [-1, 1] and solves the problem with regression method. The highest value in prediction is accepted as a target class and for multiclass data muilti-output regression is applied.
In this tutorial, we'll briefly learn how to classify data by using
Scikit-learn's RidgeClassifier class in Python. The tutorial
covers:
- Preparing the data
- Training the model
- Predicting and accuracy check
- Iris dataset classification example
- Source code listing
from sklearn.linear_model import RidgeClassifier from sklearn.datasets import load_iris
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split from sklearn.model_selection import cross_val_score from sklearn.metrics import confusion_matrix from sklearn.metrics import classification_report
Preparing the data
First,
we'll generate random classification dataset with make_classification()
function. The dataset contains 3 classes with 10 features and the
number of samples is 5000.
x, y = make_classification(n_samples=5000, n_features=10, n_classes=3, n_clusters_per_class=1)
Then, we'll split the data into train and test parts. Here, we'll extract 15 percent of it as test data.
xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.15)
Training the model
Next,
we'll define the classifier by using the RidgeClassifier class. We can use the
default parameters of the class. The parameters can be changed according
to the classification
data content.
rc = RidgeClassifier() print(rc)
RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True, max_iter=None, normalize=True, random_state=None, solver='auto', tol=0.001)
Then, we'll fit the model on train data and check the model accuracy score.
rc.fit(xtrain, ytrain) score = rc.score(xtrain, ytrain) print("Score: ", score) Score: 0.8272941176470588
We can also apply a cross-validation training method to the model and check the training score.
cv_scores = cross_val_score(rc, xtrain, ytrain, cv=10) print("CV average score: %.2f" % cv_scores.mean())
CV average score: 0.83
Predicting and accuracy check
Now, we can predict the test data by using the trained model. After the
prediction, we'll check the accuracy level by using the confusion matrix
function.
ypred = rc.predict(xtest) cm = confusion_matrix(ytest, ypred) print(cm) [[227 37 0] [ 0 190 59] [ 0 34 203]]
We can also create a classification report by using
classification_report() function on predicted data to check the other
accuracy metrics.
cr = classification_report(ytest, ypred) print(cr) precision recall f1-score support
0 1.00 0.86 0.92 264 1 0.73 0.76 0.75 249 2 0.77 0.86 0.81 237 accuracy 0.83 750 macro avg 0.83 0.83 0.83 750 weighted avg 0.84 0.83 0.83 750
Iris dataset classification example
We'll load the Iris dataset with load_iris() function, extract the x and y parts, then split into the train and test parts.
print("Iris dataset classification with SVC")
iris = load_iris() x, y = iris.data, iris.target
xtrain, xtest, ytrain, ytest=train_test_split(x, y, test_size=0.15)
Then, we'll use the same method mentioned above.
rc = RidgeClassifier()
print(rc) rc.fit(xtrain, ytrain) score = rc.score(xtrain, ytrain) print("Score: ", score) cv_scores = cross_val_score(lsvc, xtrain, ytrain, cv=10) print("CV average score: %.2f" % cv_scores.mean()) ypred = rc.predict(xtest) cm = confusion_matrix(ytest, ypred) print(cm) cr = classification_report(ytest, ypred) print(cr)
Iris dataset classification with SVC RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True, max_iter=None, normalize=False, random_state=None, solver='auto', tol=0.001) Score: 0.8818897637795275 CV average score: 0.87 [[7 0 0] [0 5 4] [0 1 6]] precision recall f1-score support 0 1.00 1.00 1.00 7 1 0.83 0.56 0.67 9 2 0.60 0.86 0.71 7 accuracy 0.78 23 macro avg 0.81 0.80 0.79 23 weighted avg 0.81 0.78 0.78 23
In this tutorial, we've briefly learned how to classify data by using
Scikit-learn's RidgeClassifier class in Python. The full source code is listed below.
Source code listing
from sklearn.linear_model import RidgeClassifier from sklearn.datasets import load_iris from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.model_selection import cross_val_score from sklearn.metrics import confusion_matrix from sklearn.metrics import classification_report x, y = make_classification(n_samples=5000, n_features=10, n_classes=3, n_clusters_per_class=1) xtrain, xtest, ytrain, ytest=train_test_split(x, y, test_size=0.15) rc = RidgeClassifier() print(rc) rc.fit(xtrain, ytrain) score = rc.score(xtrain, ytrain) print("Score: ", score) cv_scores = cross_val_score(rc, xtrain, ytrain, cv=10) print("CV average score: %.2f" % cv_scores.mean()) ypred = rc.predict(xtest) cm = confusion_matrix(ytest, ypred) print(cm) cr = classification_report(ytest, ypred) print(cr) # Iris dataset classification print("Iris dataset classification with SVC") iris = load_iris() x, y = iris.data, iris.target xtrain, xtest, ytrain, ytest=train_test_split(x, y, test_size=0.15) rc = RidgeClassifier() print(rc) rc.fit(xtrain, ytrain) score = rc.score(xtrain, ytrain) print("Score: ", score) cv_scores = cross_val_score(rc, xtrain, ytrain, cv=10) print("CV average score: %.2f" % cv_scores.mean()) ypred = rc.predict(xtest) cm = confusion_matrix(ytest, ypred) print(cm) cr = classification_report(ytest, ypred) print(cr)
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