Major Problems of Machine Learning Datasets: Part 2
Blog post from Comet
Outliers in data, which are data points that significantly differ from other values in a dataset, can affect descriptive statistics and machine learning outcomes, necessitating their detection and handling. Detection methods like standard deviation and box plots help identify outliers, which can then be managed by either removing them, replacing them with threshold values, or using the Interquartile Range (IQR) to set boundaries. Moreover, feature selection is crucial in machine learning, with techniques like Extra Trees, Mutual Information, and SelectKBest aiding in identifying the most relevant features for predictive tasks. Correlation, which measures the relationship between variables, is important to monitor as high correlation can lead to multicollinearity, which affects model performance. Techniques such as correlation matrices and setting threshold values help in identifying and removing highly correlated features to improve model accuracy.
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