Compute Metric

Compute metrics for predictions. This recipe supports following metrics: Accuracy, ROC AUC, Precision, Recall, LogLoss, MCC, MSE, RMSE, MAE, R2. Please use advanced settings to provide sample weights for metric function.

metricaccuracy

Required packages

You need below packages to use the code generated by recipe. All packages are automatically installed in MLJAR Studio.

scikit-learn>=1.5.0

Interactive recipe

You can use below interactive recipe to generate code. This recipe is available in MLJAR Studio.

In the below recipe, we assume that you have following variables available in your notebook:

y (type Series)
predicted (type DataFrame)
predicted_binary (type DataFrame)
predicted_multi (type DataFrame)

Python code

# Python code will be here

Code explanation

Compute metric to assess performance between true and predicted values.
Print computed score.

Example Python notebooks

Please find inspiration in example notebooks

Train Decision Tree on Iris data set
Python is a great choice for Machine Learning projects, because of rich ML packages ...

Scikit-learn cookbook

Code recipes from Scikit-learn cookbook.

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