In machine learning, loss functions are used to estimate how well learning algorithms perform. It is often written as loss = L(y, y_hat) where y is the true label and y_hat is predicted. Commonly used loss functions include Squared, Absolute or Laplace, Huber, Hinge, Logistic and others.
Using the Iris data set from UCIrvine, I demonstrate how contour plots of loss functions can be obtained using R.
Background Information: The Iris data after being downloaded, was pre-processed in the following manner: Only two classes (100 examples) were selected to ensure the problem remained that of binary classification. Furthermore, two attributes were selected to enable visualizations via contour plots. The glmnet package was used to build a lasso model as shown below:
The filled contour plot generated from this is shown below:
Using the Iris data set from UCIrvine, I demonstrate how contour plots of loss functions can be obtained using R.
Background Information: The Iris data after being downloaded, was pre-processed in the following manner: Only two classes (100 examples) were selected to ensure the problem remained that of binary classification. Furthermore, two attributes were selected to enable visualizations via contour plots. The glmnet package was used to build a lasso model as shown below:
The filled contour plot generated from this is shown below:
Add in your favorite loss function -- Huber or Hinge and see some nice contour plots with the Iris data!
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