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type-1-and-type-2-errors

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CyprianFusi / Predicting-Heart-Disease-using-Logistic-Regression-Classification-Algorithm

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With a precision of 86% and model's CAP curve showing an accuracy of 100%! This means it is capable of correctly predicting 100% of patients with a heart disease after processing 50% of the data. The model's performance is "Too Good to be True"! However, with Train accuracy = 86% and Test accuracy = 82%, there is no visible sign of overfitting.

logistic-regression precision false-positive heart-disease false-negative type-1-and-type-2-errors cap-curve
  • Updated Jun 22, 2024
  • Jupyter Notebook

Ninad077 / Machine_Learning-Logistic_Regression

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Content: Machine Learning, Logistic regression steps, Probability matrix, Confusion matrix, Accuracy score, Recall value, Data preprocessing, Label encoding, Scaling the data, Splitting train test data, Running Logistic Regression, Y prediction on test data, Class imbalance, Type 1 & Type 2 errors.

machine-learning confusion-matrix data-preprocessing class-imbalance label-encoding accuracy-score probability-matrix recall-value scaling-the-data splitting-train-test-data y-prediction-on-test-data type-1-and-type-2-errors
  • Updated May 6, 2024
  • Jupyter Notebook

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