How to evaluate ML models ? Difference between Bagging and Boosting ? What is F1 score and how change weights of Precision or recall while calculating F1 score ? What does it mean to have F1 score value 1 ?

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ML models can be evaluated using metrics like accuracy, precision, recall, F1 score. Bagging combines multiple models, while boosting focuses on correcting errors. F1 score balances precision and recall.

ML models can be evaluated using metrics like accuracy, precision, recall, and F1 score.
Bagging is an ensemble technique where multiple models are trained independently and then combined by averaging or voting.
Boosting is an ensemble technique where models are trained sequentially, with each model correcting errors made by the previous ones.
F1 score is a metric that balances precision and recall, calculated as 2 * (precision * recall) / (precision + recall).
To change weights of precision or recall while calculating F1 score, you can adjust the threshold for classification.
Having an F1 score value of 1 means perfect precision and recall, indicating a model that makes no false positives or false negatives.

How to evaluate ML models ? Difference between Bagging and Boosting ? What is F1 score and how change weights of Precision or recall while calculating F1 score ? What does it mean to have F1 score value 1 ?

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