figure - sample decision tree model the decision tree is easy to interpret by following the path with the values of an unknown data point the leaf node where you end up is the predicted class for the data point the model parameters in this case are the attributes on which we are splitting (here sexageand sibspand the values of those attributes for example if person was femaleit is likely she had survived based on this model howeverinfant males having age less than years and months are likely to have perished in the algorithmthe decision whether we will continue splitting the dataset at node further or stop the splitting process is governed by one of the hyperparameters of the algorithm named min_samples_leaf this is hyperparameter associated with the decision tree algorithm the default value of this parameter is which means that we can potentially keep splitting the data until we have leaf node with single data point (with unique class labelthis leads to lot of overfitting as potentially each data point can end up in its own leaf node and the model will not learn anything useful suppose we want to stop the splitting process if we have - of the whole dataset in leaf node and label that node with the majority class of that node this can be achieved by setting different value for the specified hyperparameter this allows us to control the overfitting and help us develop generalized model this is just one of the hyperparameters associated with the algorithmthere are many more like the splitting criterion (criterion)maximum depth of the tree (max_depth)number of features (max_features)and so onwhich can have different effects on the quality of the overall model similar hyperparameters exist for each learning algorithm examples include the learning rate in logistic regressionthe kernel in svmsand the dropout rate in neural networks hyperparameters are generally closely related to the learning algorithm hence we require some understanding of the algorithm to have intuition about setting the value of particular hyperparameter in the later sections of this and the bookwe deal with datasets and models that will require some level of hyperparameter tuning the bias-variance tradeoff so farwe learned about the necessary concepts which talk about tuning our models but before go into the process of putting it all together and actually tuning our modelswe must understand potential tradeoff that puts some restriction on the best model that we can develop this tradeoff is called the bias versus variance tradeoff the obvious question that arises is what are bias and variance in the context of machine learning models