Using Log Loss
Log loss seeks to calculate how uncertain your model is about the predictions it is generating.your model could predict with 80% certainty that a customer is going to buy a t-shirt. Log loss enables you to measure how strongly the model believes that its prediction is accurate.
Accuracy
is the fraction of predictions a model gets right.
Recall, Precision, Mean Absolute Error, Log Loss, R2, F1 Score, KL Divergence, Quality Loss, High Loss.
There are many different tools that can be used to evaluate a linear regression model. Here are a few examples:
Mean absolute error (MAE): This is measured by taking the average of the absolute difference between the actual values and the predictions. Ideally, this difference is minimal.
Root mean square error (RMSE): This is similar MAE, but takes a slightly modified approach so values with large error receive a higher penalty. RMSE takes the square root of the average squared difference between the prediction and the actual value.
Coefficient of determination or R-squared (R^2): This measures how well-observed outcomes are actually predicted by the model, based on the proportion of total variation of outcomes.
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