Gist for "Learn ML Algorithms by coding" Blog

decision_tree_crude_v1.py

class DecisionTree:

    def fit(self, data, target):
        self.data = data
        self.target = target
        self.independent = self.data.columns.tolist()
        self.independent.remove(target)
    
    def predict(self, data):
        return np.array([self.__flow_data_thru_tree(row) for row in data.values])

    def __flow_data_thru_tree(self, row):
        return self.data[self.target].value_counts() \
                   .apply(lambda x: x/len(self.data)).tolist()