In this chapter, you learned how to evaluate the performances of the three different types of machine learning algorithms: classification, regression, and unsupervised.
For the classification algorithms, you learned how to evaluate the performance of a model by using a series of visual techniques, such as the confusion matrix, normalized confusion matrix, area under the curve, K-S statistic plot, cumulative gains plot, lift curve, calibration plot, learning curve, and cross-validated box plot.
For the regression algorithms, you learned how to evaluate the performance of a model by using three metrics: the mean squared error, mean absolute error, and root mean squared error.
Finally, for the unsupervised machine learning algorithms, you learned how to evaluate the performance of a model by using the elbow plot.
Congratulations! You have now made it to the end of your...
