Visualizing Outliers in 2D Space
Visualization is a crucial tool for understanding how outliers are distributed in your data, especially when working with two-dimensional datasets. By plotting your data, you can directly observe clusters, trends, and unusual points that may not be apparent from summary statistics alone. Visual inspection often provides the first indication of whether your data contains isolated outliers, clustered anomalies, or more subtle deviations from expected patterns. This can guide your selection of detection methods and help you interpret the results of automated algorithms.
1234567891011121314151617181920212223import numpy as np import matplotlib.pyplot as plt # Generate normal data rng = np.random.RandomState(42) X_normal = rng.normal(loc=0, scale=1, size=(100, 2)) # Inject outliers X_outliers = rng.uniform(low=-6, high=6, size=(8, 2)) # Combine data X_combined = np.vstack([X_normal, X_outliers]) # Plot plt.figure(figsize=(7, 7)) plt.scatter(X_normal[:, 0], X_normal[:, 1], c="blue", label="Normal data") plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c="red", label="Outliers", edgecolor="black", s=80) plt.xlabel("Feature 1") plt.ylabel("Feature 2") plt.title("2D Scatter Plot with Injected Outliers") plt.legend() plt.grid(True) plt.show()
Visual patterns in 2D plots can reveal different types of anomalies. Isolated points far from the main cluster may indicate global outliers, while small groups of points separated from the majority could signal local anomalies. Sometimes, outliers may align along a particular direction or pattern, suggesting systematic deviations. Recognizing these patterns visually helps you understand the nature of anomalies present and whether they are due to random noise, data entry errors, or underlying structure in the data.
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Visualization is a crucial tool for understanding how outliers are distributed in your data, especially when working with two-dimensional datasets. By plotting your data, you can directly observe clusters, trends, and unusual points that may not be apparent from summary statistics alone. Visual inspection often provides the first indication of whether your data contains isolated outliers, clustered anomalies, or more subtle deviations from expected patterns. This can guide your selection of detection methods and help you interpret the results of automated algorithms.
1234567891011121314151617181920212223import numpy as np import matplotlib.pyplot as plt # Generate normal data rng = np.random.RandomState(42) X_normal = rng.normal(loc=0, scale=1, size=(100, 2)) # Inject outliers X_outliers = rng.uniform(low=-6, high=6, size=(8, 2)) # Combine data X_combined = np.vstack([X_normal, X_outliers]) # Plot plt.figure(figsize=(7, 7)) plt.scatter(X_normal[:, 0], X_normal[:, 1], c="blue", label="Normal data") plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c="red", label="Outliers", edgecolor="black", s=80) plt.xlabel("Feature 1") plt.ylabel("Feature 2") plt.title("2D Scatter Plot with Injected Outliers") plt.legend() plt.grid(True) plt.show()
Visual patterns in 2D plots can reveal different types of anomalies. Isolated points far from the main cluster may indicate global outliers, while small groups of points separated from the majority could signal local anomalies. Sometimes, outliers may align along a particular direction or pattern, suggesting systematic deviations. Recognizing these patterns visually helps you understand the nature of anomalies present and whether they are due to random noise, data entry errors, or underlying structure in the data.
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