Python Data Science & AI | Machine Learning | Lecture 17: Outliers

In this lecture, we explore the concept of outliers in the context of data analysis and machine learning. Outliers are data points that deviate significantly from the majority of observations in a dataset. Understanding and handling outliers is crucial as they can skew results, affect model performance, and distort data insights.

Definition of Outliers: Outliers are unusual data points that fall outside the expected range of values. They may result from variability in the data, measurement errors, or other anomalies.

Types of Outliers:

Univariate Outliers: Outliers in one variable or feature.
Multivariate Outliers: Unusual combinations of values across multiple features.
Detection Techniques:

Visualization: Using box plots, scatter plots, or histograms to visually identify outliers.
Statistical Methods:
Z-scores (Standard deviation method)
Interquartile Range (IQR)
Modified Z-scores
Impact of Outliers:

Influence on central tendency (mean, median)
Effects on machine learning models (overfitting, inaccurate predictions)
Handling Outliers:

Removing: In cases where the outliers are due to errors.
Capping/Imputation: Replacing outliers with thresholds or mean/median values.
Transformation: Applying log, square root, or other transformations to minimize the effect of outliers.
Outlier Detection in Machine Learning:

The role of outliers in different algorithms (e.g., linear regression vs. tree-based models).
How to deal with outliers during model building and evaluation.
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