Grid search in machine learning is a popular way to tune the hyperparameters of the model in order to find the best combination for determining the best fit:
In the following code, implementation has been performed to determine whether a particular user will click an ad or not. Grid search has been implemented using a decision tree classifier for classification purposes. Tuning parameters are the depth of the tree, the minimum number of observations in terminal node, and the minimum number of observations required to perform the node split:
# Grid search >>> import pandas as pd >>> from sklearn.tree import DecisionTreeClassifier ...
