outliers missing values, overfitting underfitting

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Understanding outliers, missing values, overfitting, and underfitting in data science.

Outliers are data points that significantly differ from other data points in a dataset.
Missing values are data points that are not present in a dataset.
Overfitting occurs when a model learns noise in the training data rather than the underlying pattern.
Underfitting occurs when a model is too simple to capture the underlying pattern in the data.
Examples: Outliers - a temperature sensor malfunctioning and recording an extremely high temperature. Missing values - a survey respondent not answering a question. Overfitting - a decision tree with too many branches fitting the noise in the data. Underfitting - a linear regression model trying to fit a non-linear relationship.

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