Markov Chains, Computer Science, Programming, Probability, Problem Solving.
plt.ylabel('Value') plt.legend() plt.show() return y_pred def fit_decision_tree(self): # Transform the data into the required format X = np.arange(len(self.chainList)).reshape(-1, 1) y = np.array(self.numericData) # Split the data into training and testing sets X_train, X_test, y_train, y_test =