criterion − string, optional default= “gini”

It represents the function to measure the quality of a split. Supported criteria are “gini” and “entropy”. The default is gini which is for Gini impurity while entropy is for the information gain.

splitter − string, optional default= “best”

It tells the model, which strategy from “best” or “random” to choose the split at each node.

max_depth − int or None, optional default=None

This parameter decides the maximum depth of the tree. The default value is None which means the nodes will expand until all leaves are pure or until all leaves contain less than min_smaples_split samples.

min_samples_split − int, float, optional default=2

This parameter provides the minimum number of samples required to split an internal node.

min_samples_leaf − int, float, optional default=1

This parameter provides the minimum number of samples required to be at a leaf node.

min_weight_fraction_leaf − float, optional default=0.

With this parameter, the model will get the minimum weighted fraction of the sum of weights required to be at a leaf node.

max_features − int, float, string or None, optional default=None

It gives the model the number of features to be considered when looking for the best split.

random_state − int, RandomState instance or None, optional, default = none

This parameter represents the seed of the pseudo random number generated which is used while shuffling the data. Followings are the options −

• int − In this case, random_state is the seed used by random number generator.

• RandomState instance − In this case, random_state is the random number generator.

• None − In this case, the random number generator is the RandonState instance used by np.random.

max_leaf_nodes − int or None, optional default=None

This parameter will let grow a tree with max_leaf_nodes in best-first fashion. The default is none which means there would be unlimited number of leaf nodes.

min_impurity_decrease − float, optional default=0.

This value works as a criterion for a node to split because the model will split a node if this split induces a decrease of the impurity greater than or equal to min_impurity_decrease value.

min_impurity_split − float, default=1e-7

It represents the threshold for early stopping in tree growth.

class_weight − dict, list of dicts, “balanced” or None, default=None

It represents the weights associated with classes. The form is {class_label: weight}. If we use the default option, it means all the classes are supposed to have weight one. On the other hand, if you choose class_weight: balanced, it will use the values of y to automatically adjust weights.

presort − bool, optional default=False

It tells the model whether to presort the data to speed up the finding of best splits in fitting. The default is false but of set to true, it may slow down the training process.

feature_importances_ − array of shape =[n_features]

This attribute will return the feature importance.

classes_: − array of shape = [n_classes] or a list of such arrays

It represents the classes labels i.e. the single output problem, or a list of arrays of class labels i.e. multi-output problem.

max_features_ − int

It represents the deduced value of max_features parameter.

n_classes_ − int or list

It represents the number of classes i.e. the single output problem, or a list of number of classes for every output i.e. multi-output problem.

n_features_ − int

It gives the number of features when fit() method is performed.

n_outputs_ − int

It gives the number of outputs when fit() method is performed.

apply(self, X[, check_input])

This method will return the index of the leaf.

decision_path(self, X[, check_input])

As name suggests, this method will return the decision path in the tree

fit(self, X, y[, sample_weight, …])

fit() method will build a decision tree classifier from given training set (X, y).

get_depth(self)

As name suggests, this method will return the depth of the decision tree

get_n_leaves(self)

As name suggests, this method will return the number of leaves of the decision tree.

get_params(self[, deep])

We can use this method to get the parameters for estimator.

predict(self, X[, check_input])

It will predict class value for X.

predict_log_proba(self, X)

It will predict class log-probabilities of the input samples provided by us, X.

predict_proba(self, X[, check_input])

It will predict class probabilities of the input samples provided by us, X.

score(self, X, y[, sample_weight])

As the name implies, the score() method will return the mean accuracy on the given test data and labels..

set_params(self, \*\*params)

We can set the parameters of estimator with this method.