Multicollinearity occurs in regression analysis when two or more predictor variables are highly correlated, making it difficult to isolate the individual effect of each predictor on the response variable. This can lead to inflated standard errors and unreliable statistical inferences, complicating model interpretation and reducing the precision of estimated coefficients.

Multicollinearity

Regression analysis is a statistical method used to model and analyze the relationships between a dependent variable and one or more independent variables. It helps in predicting outcomes and identifying the strength and nature of relationships, making it a fundamental tool in data analysis and predictive modeling.

regression analysis

Predictor variables, also known as independent variables, are factors that are used in statistical models to predict or explain variations in a dependent variable. They are crucial in regression analysis and other predictive modeling techniques to determine the strength and nature of relationships between variables.

predictor variables

A response variable, also known as a dependent variable, is the outcome or subject of interest in a study or experiment that researchers aim to explain or predict. It is influenced by one or more independent variables, and its changes are analyzed to understand relationships and causal effects within the data.

response variable

inflated standard errors

unreliable statistical inferences

Model interpretation refers to the process of understanding and explaining how a machine learning model makes predictions, which is crucial for building trust, ensuring fairness, and improving model performance. It involves techniques and tools that provide insights into the model's decision-making process, enabling stakeholders to assess its reliability and biases.

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