10 Regression Metrics Data Scientist Must Know (Python-Sklearn Code Included)

Source: Unsplash

import numpy as np
from sklearn.metrics import *

Python Functions

1. Mean Absolute Error

Definition from Wikipedia:

Mean Absolute Error (MAE) is a measure of errors between paired observations expressing the same phenomenon. Examples of Y versus X include comparisons of predicted versus observed, subsequent time versus initial time, and one technique of measurement versus an alternative technique of measurement.

Formula:

def mae(y_true, y_pred):
    """
    Mean absolute error regression loss.
    Args:
        y_true ([np.array]): test samples
        y_pred ([np.array]): predicted samples
    Returns:
        [float]: mean absolute error
    """
    return mean_absolute_error(y_true, y_pred)

2. Mean Square Error

Definition from Wikipedia:

Mean squared error (MSE) of an estimator measures the average of the squares of the errors — that is, the average squared difference between the estimated values and the actual value.

Formula:

def mse(y_true, y_pred):
    """
    Mean squared…