Tune Hyperparameter

Tune the hyperparameter with the help of A.I.

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I want you to act as a data scientist and code for me. I have trained a [model name]. Please write the code to tune the hyper parameters.

Example answer

  • Set the number of iterations: model.set_params(n_iter = [number])
  • Set the maximum depth of each tree: model.set_params(max_depth=[number])
  • Set the minimum information gain for each split: model.set_params(min_impurity_decrease=[number])
  • Set the minimum number of samples required to split a node: model.set_params(min_samples_split=[number])
  • Set the minimum number of samples required at each leaf node: model.set_params(min_samples_leaf=[number])
  • Set the maximum features to consider when looking for the best split: model.set_params(max_features=[number])

#Import the relevant libraries 
import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt 
from [model_name] import [model_name] 

#Define the model 
model = [model_name]() 

#Define the parameters to tune 
params = {'parameter_1':[0.2,0.4,0.6,0.8], 
          'parameter_2':[5, 10, 15, 20], 
          'parameter_3':[10, 15, 20, 25] 

#Train model using grid search 
from sklearn.model_selection import GridSearchCV 
clf = GridSearchCV(model, params, cv=3, scoring='accuracy') 
clf.fit(X, y) 

#Display the best parameters 
print("The best parameters are: " + str(clf.best_params_))