Precision & Recall Precision and Recall are essential metrics for evaluating binary classification models. Precision measures the fraction of positive predictions that were correct. In other words, it quantifies how accurately the model predicts customers who are likely to churn. Precision = True Positives / (# Positive Predictions) = True Positives / (True Positives +Continue reading “ML Zoomcamp 2023 – Evaluation metrics for classification– Part 4”
Tag Archives: Accuracy
ML Zoomcamp 2023 – Evaluation metrics for classification– Part 2
Accuracy and Dummy Model In the last article, we calculated that our model achieved an accuracy of 80% on the validation data. Now, let’s determine whether this is a good value or not. Accuracy measures the fraction of correct predictions made by the model. In our evaluation, we checked each customer in the validation datasetContinue reading “ML Zoomcamp 2023 – Evaluation metrics for classification– Part 2”
ML Zoomcamp 2023 – Machine Learning for Classification– Part 10
Training logistic regression with Scikit-Learn Train a model with Scikit-Learn When you want to train a logistic regression model, the process is quite similar to training a linear regression model. You can use the ‘coef_’ attribute to display the weights (coefficients) in a logistic regression model. The ‘coef_’ attribute in logistic regression returns a 2-dimensionalContinue reading “ML Zoomcamp 2023 – Machine Learning for Classification– Part 10”
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