gaitsetpy.classification.utils.eval

For evaluation of a classification model

Maintainer: @aharshit123456

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 1'''
 2For evaluation of a classification model
 3
 4Maintainer: @aharshit123456
 5'''
 6
 7from sklearn.metrics import accuracy_score, confusion_matrix
 8from sklearn.model_selection import train_test_split
 9from .preprocess import preprocess_features
10
11def evaluate_model(model, features):
12    """
13    Evaluates the given model on the provided features and prints accuracy and confusion matrix.
14    """
15    X, y = preprocess_features(features)
16    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
17
18    y_pred = model.predict(X_test)
19
20    acc = accuracy_score(y_test, y_pred)
21    # conf_matrix = confusion_matrix(y_test, y_pred)
22
23    print(f"Accuracy: {acc:.4f}")
24    # print(f"Confusion Matrix:\n{conf_matrix}")

def evaluate_model(model, features): View Source

12def evaluate_model(model, features):
13    """
14    Evaluates the given model on the provided features and prints accuracy and confusion matrix.
15    """
16    X, y = preprocess_features(features)
17    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
18
19    y_pred = model.predict(X_test)
20
21    acc = accuracy_score(y_test, y_pred)
22    # conf_matrix = confusion_matrix(y_test, y_pred)
23
24    print(f"Accuracy: {acc:.4f}")
25    # print(f"Confusion Matrix:\n{conf_matrix}")

Evaluates the given model on the provided features and prints accuracy and confusion matrix.