Python - Python Machine Learning - Standard Deviation Part 3: Importance of Standard Deviation in Machine Learning

Why is Standard Deviation Important?

Detecting Outliers: Data points that lie far from the mean indicate anomalies.

Feature Scaling: Many ML algorithms require standardized input, often achieved using standard deviation.

Model Performance Analysis: Evaluates the consistency of a model’s predictions.

Example 4: Identifying Outliers Using Standard Deviation

import numpy as np

data = [10, 12, 15, 14, 13, 110, 16, 18]  # 110 is an outlier

mean = np.mean(data)

std_dev = np.std(data)

outliers = [x for x in data if abs(x - mean) > 2 * std_dev]

print("Outliers:", outliers)

Output:

Outliers: [110]

Explanation:

Any value more than 2 standard deviations from the mean is considered an outlier.

Here, 110 is significantly different from other values.