Next, you’ll cover recipes for using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors and generate visualizations for exploratory data analysis (EDA) to identify unexpected values. Finally, you’ll build functions and classes that you can reuse ...
This can be achieved by discretization or binning values into a fixed number of buckets. This can reduce the number of unique values for each feature from tens of thousands down to a few hundred. This allows the decision tree to operate upon the ordinal bucket (an integer) instead of ...
PiML also works for arbitrary supervised ML models under regression and binary classification settings. It supports a whole spectrum of outcome testing, including but not limited to the following: Accuracy: popular metrics like MSE, MAE for regression tasks and ACC, AUC, Recall, Precision, F1-sco...
For instance, consider Figure 4-75, which includes images of different faces, an example often used in supervised machine learning problems (for more information, see “In-Depth: Support Vector Machines”): In[6]: fig, ax = plt.subplots(5, 5, figsize=(5, 5)) fig.subplots_adjust(hspace...
Next, you’ll cover recipes for using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors and generate visualizations for exploratory data analysis (EDA) to identify unexpected values. Finally, you’ll build functions and classes that you can reuse ...