Common Metrics

easy· Machine Learningruns: 0

Implement the common regression and classification metrics used to evaluate models: mean squared error, mean absolute error, r-squared, accuracy, and precision/recall/F1 for binary classification.

wpm 0acc 100%time 0:000 / 818

import numpy as np

def mse(y_true, y_pred):
    return np.mean((y_true - y_pred) ** 2)

def mae(y_true, y_pred):
    return np.mean(np.abs(y_true - y_pred))

def r2_score(y_true, y_pred):
    ss_res = np.sum((y_true - y_pred) ** 2)
    ss_tot = np.sum((y_true - np.mean(y_true)) ** 2)
    return 1 - ss_res / ss_tot

def accuracy(y_true, y_pred):
    return np.mean(y_true == y_pred)

def precision_recall_f1(y_true, y_pred):
    tp = np.sum((y_true == 1) & (y_pred == 1))
    fp = np.sum((y_true == 0) & (y_pred == 1))
    fn = np.sum((y_true == 1) & (y_pred == 0))
    precision = tp / (tp + fp) if tp + fp else 0.0
    recall = tp / (tp + fn) if tp + fn else 0.0
    f1 = (
        2 * precision * recall / (precision + recall)
        if precision + recall
        else 0.0
    )
    return precision, recall, f1

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