What are outliers and how do you detect and handle them?

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Outliers are data points that differ significantly from the majority of observations in a dataset. They can result from errors, variability in measurements, or genuine but rare events. Outliers can distort analysis, affecting statistical summaries and machine learning models.

How to Detect Outliers:

  1. Visual Methods:

    • Boxplots: Show data distribution and highlight points outside the whiskers as outliers.

    • Scatter plots: Identify points far from clusters.

    • Histograms: Reveal unusual gaps or spikes.

  2. Statistical Methods:

    • Z-score: Measures how many standard deviations a point is from the mean; commonly, points with |Z| > 3 are outliers.

    • IQR (Interquartile Range): Points outside 1.5 × IQR below Q1 or above Q3 are outliers.

    • Mahalanobis Distance: Detects multivariate outliers considering correlations between variables.

  3. Model-based Methods:

    • Use algorithms like Isolation Forest or DBSCAN to flag anomalies.

How to Handle Outliers:

  1. Investigate: Determine if the outlier is a data entry error or a valid extreme value.

  2. Remove: If erroneous or irrelevant, exclude outliers.

  3. Transform: Apply transformations (e.g., log) to reduce their effect.

  4. Cap or Winsorize: Limit extreme values to a certain percentile.

  5. Use Robust Models: Choose algorithms less sensitive to outliers, like tree-based models.

Handling outliers carefully improves model accuracy and data integrity without discarding meaningful information.

Read More

How does a Random Forest algorithm work?

What is dimensionality reduction? Explain PCA (Principal Component Analysis).

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