machine-learning

6 articles

• What happens in anomaly detection?

  Anomaly detection is the process of identifying unusual patterns or events in data that do not conform to an expected behavior

  January 4, 2023

• Forms of Discriminant Analysis Explained in Details

  Discriminant Analysis is a powerful tool for understanding and predicting the relationships between variables

  January 3, 2023

• How to implement custom loss function in scikit-learn?

  To implement a custom loss function in scikit-learn, we'll need to use the make_scorer function

  December 25, 2022

• How to use XGBoost in Python?

  Use XGBoost in Python via its scikit-learn API (XGBClassifier/Regressor) or native DMatrix API. Key hyperparameters and early stopping explained.

  December 24, 2022

• transform vs fit_transform in scikit-learn: The Data-Leakage Rule (and Why You Should Use a Pipeline)

  fit vs transform vs fit_transform in scikit-learn, and the rule that prevents data leakage: fit on train only, transform train and test with the training parameters. Plus why a Pipeline is the correct way to enforce this.

  December 21, 2022

• Difference between StratifiedKFold and StratifiedShuffleSplit in sklearn

  StratifiedKFold makes k non-overlapping folds (every sample tested once); StratifiedShuffleSplit draws independent random splits. When to use each.

  December 20, 2022

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