introduction to hyperparameters what are hyperparametersthe simplest definition is that hyperparameters are meta parameters that are associated with any machine learning algorithm and are usually set before the model training and building process we do this because model hyperparameters do not have any dependency on being derived from the underlying dataset on which model is trained hyperparameters are extremely important for tuning the performance of learning algorithms hyperparameters are often confused with model parametersbut we must keep in mind that hyperparameters are different than model parameters since they do not have dependency on the data in simple termsmodel hyperparameters represent some high-level concepts or knobs that data scientist can tweak and tune during the model training and building process to improve its performance let' take an example to illustrate this in case you still have difficulty in interpreting them ecision trees decision trees are one of the simplest and easy to interpret classification algorithms (also used in regression sometimescheck out cart modelsfirst you will learn how decision tree is created as hyperparameters are often tightly coupled with the actual intricacies of the algorithm the decision tree algorithm is based on greedy recursive partitioning of the initial dataset (featuresit leverages decision tree based structure for taking decisions of how to perform the partitions the steps involved in learning decision tree are as follows start with whole dataset and find the attribute (featurethat will best differentiate between the classes this best attribute is found out using metrics such as information gain or gini impurity once the best attribute is foundseparate the dataset in two (or more partsbased on the values of the attributes if any one part of the dataset contains only labels of one classwe can stop the process for that part and label it as leaf node of that class we repeat the whole process until we have leaf nodes in allof which we have data points of one class only the final model returned by the decision tree algorithm can be represented as flow chart (the core decision tree structureconsider sample decision tree for the titanic survival prediction problem depicted in figure -