How to find optimal parameters for CatBoost using GridSearchCV for Regression?
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How to find optimal parameters for CatBoost using GridSearchCV for Regression?

How to find optimal parameters for CatBoost using GridSearchCV for Regression?

This recipe helps you find optimal parameters for CatBoost using GridSearchCV for Regression

2

Recipe Objective

Many a times while working on a dataset and using a Machine Learning model we don't know which set of hyperparameters will give us the best result. Passing all sets of hyperparameters manually through the model and checking the result might be a hectic work and may not be possible to do.

To get the best set of hyperparameters we can use Grid Search. Grid Search passes all combinations of hyperparameters one by one into the model and check the result. Finally it gives us the set of hyperparemeters which gives the best result after passing in the model.

This python source code does the following:
1. pip install Catboost
2. Imports SKlearn dataset
3. Performs validation dataset from the existing dataset
4. Applies Catboost Regressor
5. Hyperparameter tuning using GridSearchCV

So this recipe is a short example of how we can find optimal parameters for CatBoost using GridSearchCV for Regression.

Step 1 - Import the library - GridSearchCv

from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.model_selection import GridSearchCV from catboost import CatBoostRegressor

Here we have imported various modules like datasets, CatBoostRegressor and GridSearchCV from differnt libraries. We will understand the use of these later while using it in the in the code snipet.
For now just have a look on these imports.

Step 2 - Setup the Data

Here we have used datasets to load the inbuilt iris dataset and we have created objects X and y to store the data and the target value respectively. dataset = datasets.load_iris() X = dataset.data; y = dataset.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)

Step 3 - Model and its Parameter

Here, we are using CatBoostRegressor as a Machine Learning model to use GridSearchCV. So we have created an object model_CBR. model_CBR = CatBoostRegressor() Now we have defined the parameters of the model which we want to pass to through GridSearchCV to get the best parameters. So we are making an dictionary called parameters in which we have four parameters learning_rate, depth and iteration. parameters = {'depth' : [6,8,10], 'learning_rate' : [0.01, 0.05, 0.1], 'iterations' : [30, 50, 100] }

Step 4 - Using GridSearchCV and Printing Results

Before using GridSearchCV, lets have a look on the important parameters.

  • estimator: In this we have to pass the models or functions on which we want to use GridSearchCV
  • param_grid: Dictionary or list of parameters of models or function in which GridSearchCV have to select the best.
  • Scoring: It is used as a evaluating metric for the model performance to decide the best hyperparameters, if not especified then it uses estimator score.
  • cv : In this we have to pass a interger value, as it signifies the number of splits that is needed for cross validation. By default is set as five.
  • n_jobs : This signifies the number of jobs to be run in parallel, -1 signifies to use all processor.
Making an object grid_GBC for GridSearchCV and fitting the dataset i.e X and y grid = GridSearchCV(estimator=model_CBR, param_grid = parameters, cv = 2, n_jobs=-1) grid.fit(X_train, y_train) Now we are using print statements to print the results. It will give the values of hyperparameters as a result. print(" Results from Grid Search " ) print("\n The best estimator across ALL searched params:\n", grid.best_estimator_) print("\n The best score across ALL searched params:\n", grid.best_score_) print("\n The best parameters across ALL searched params:\n", grid.best_params_) As an output we get:

0:	learn: 0.7716436	total: 46.3ms	remaining: 4.58s
1:	learn: 0.7414652	total: 46.6ms	remaining: 2.28s
2:	learn: 0.7125578	total: 47.2ms	remaining: 1.52s
3:	learn: 0.6871347	total: 47.6ms	remaining: 1.14s
4:	learn: 0.6621916	total: 48.3ms	remaining: 918ms
5:	learn: 0.6370111	total: 50.7ms	remaining: 794ms
6:	learn: 0.6165412	total: 51.1ms	remaining: 678ms
7:	learn: 0.5926945	total: 51.4ms	remaining: 591ms
8:	learn: 0.5704622	total: 51.7ms	remaining: 523ms
9:	learn: 0.5497470	total: 52.1ms	remaining: 469ms
10:	learn: 0.5285706	total: 52.5ms	remaining: 424ms
11:	learn: 0.5102976	total: 52.9ms	remaining: 388ms
12:	learn: 0.4927243	total: 53.3ms	remaining: 357ms
13:	learn: 0.4767788	total: 53.8ms	remaining: 330ms
14:	learn: 0.4584534	total: 54.2ms	remaining: 307ms
15:	learn: 0.4416577	total: 54.6ms	remaining: 287ms
16:	learn: 0.4258021	total: 55ms	remaining: 269ms
17:	learn: 0.4106832	total: 55.4ms	remaining: 252ms
18:	learn: 0.3974296	total: 55.8ms	remaining: 238ms
19:	learn: 0.3869505	total: 56.2ms	remaining: 225ms
20:	learn: 0.3731509	total: 56.6ms	remaining: 213ms
21:	learn: 0.3615482	total: 57ms	remaining: 202ms
22:	learn: 0.3501406	total: 57.4ms	remaining: 192ms
23:	learn: 0.3386937	total: 57.8ms	remaining: 183ms
24:	learn: 0.3269810	total: 58.1ms	remaining: 174ms
25:	learn: 0.3172767	total: 58.5ms	remaining: 166ms
26:	learn: 0.3078365	total: 58.9ms	remaining: 159ms
27:	learn: 0.2989866	total: 59.4ms	remaining: 153ms
28:	learn: 0.2907521	total: 59.7ms	remaining: 146ms
29:	learn: 0.2820723	total: 60.1ms	remaining: 140ms
30:	learn: 0.2732105	total: 60.5ms	remaining: 135ms
31:	learn: 0.2658956	total: 60.9ms	remaining: 129ms
32:	learn: 0.2597752	total: 61.3ms	remaining: 124ms
33:	learn: 0.2519285	total: 61.7ms	remaining: 120ms
34:	learn: 0.2449226	total: 62.1ms	remaining: 115ms
35:	learn: 0.2396648	total: 62.5ms	remaining: 111ms
36:	learn: 0.2327188	total: 62.9ms	remaining: 107ms
37:	learn: 0.2271869	total: 63.3ms	remaining: 103ms
38:	learn: 0.2212449	total: 63.7ms	remaining: 99.7ms
39:	learn: 0.2160455	total: 64.1ms	remaining: 96.2ms
40:	learn: 0.2105444	total: 64.5ms	remaining: 92.9ms
41:	learn: 0.2049493	total: 65ms	remaining: 89.7ms
42:	learn: 0.1992581	total: 65.3ms	remaining: 86.6ms
43:	learn: 0.1950601	total: 65.7ms	remaining: 83.7ms
44:	learn: 0.1905929	total: 66.1ms	remaining: 80.8ms
45:	learn: 0.1864159	total: 66.6ms	remaining: 78.1ms
46:	learn: 0.1827717	total: 66.9ms	remaining: 75.4ms
47:	learn: 0.1787064	total: 67.3ms	remaining: 72.9ms
48:	learn: 0.1747380	total: 67.8ms	remaining: 70.6ms
49:	learn: 0.1712025	total: 68.2ms	remaining: 68.2ms
50:	learn: 0.1677444	total: 68.7ms	remaining: 66ms
51:	learn: 0.1644736	total: 69.1ms	remaining: 63.8ms
52:	learn: 0.1615005	total: 69.5ms	remaining: 61.6ms
53:	learn: 0.1589166	total: 69.9ms	remaining: 59.6ms
54:	learn: 0.1561049	total: 70.3ms	remaining: 57.5ms
55:	learn: 0.1535928	total: 71.1ms	remaining: 55.8ms
56:	learn: 0.1507811	total: 71.5ms	remaining: 53.9ms
57:	learn: 0.1490974	total: 72.4ms	remaining: 52.4ms
58:	learn: 0.1466473	total: 72.8ms	remaining: 50.6ms
59:	learn: 0.1455853	total: 73ms	remaining: 48.7ms
60:	learn: 0.1433409	total: 73.4ms	remaining: 46.9ms
61:	learn: 0.1413980	total: 73.8ms	remaining: 45.2ms
62:	learn: 0.1399844	total: 74.6ms	remaining: 43.8ms
63:	learn: 0.1372188	total: 75ms	remaining: 42.2ms
64:	learn: 0.1356385	total: 75.4ms	remaining: 40.6ms
65:	learn: 0.1327448	total: 77.5ms	remaining: 39.9ms
66:	learn: 0.1303423	total: 77.8ms	remaining: 38.3ms
67:	learn: 0.1277835	total: 78.3ms	remaining: 36.8ms
68:	learn: 0.1261218	total: 78.7ms	remaining: 35.3ms
69:	learn: 0.1236443	total: 79.1ms	remaining: 33.9ms
70:	learn: 0.1217924	total: 79.5ms	remaining: 32.5ms
71:	learn: 0.1204415	total: 79.9ms	remaining: 31.1ms
72:	learn: 0.1190207	total: 80.3ms	remaining: 29.7ms
73:	learn: 0.1174460	total: 80.7ms	remaining: 28.3ms
74:	learn: 0.1159067	total: 81ms	remaining: 27ms
75:	learn: 0.1148673	total: 81.5ms	remaining: 25.7ms
76:	learn: 0.1128619	total: 81.9ms	remaining: 24.5ms
77:	learn: 0.1114413	total: 82.3ms	remaining: 23.2ms
78:	learn: 0.1098260	total: 82.7ms	remaining: 22ms
79:	learn: 0.1085060	total: 83.2ms	remaining: 20.8ms
80:	learn: 0.1071127	total: 83.6ms	remaining: 19.6ms
81:	learn: 0.1056581	total: 84ms	remaining: 18.4ms
82:	learn: 0.1041074	total: 84.4ms	remaining: 17.3ms
83:	learn: 0.1028824	total: 84.7ms	remaining: 16.1ms
84:	learn: 0.1011530	total: 85.1ms	remaining: 15ms
85:	learn: 0.1007926	total: 85.3ms	remaining: 13.9ms
86:	learn: 0.0994513	total: 85.8ms	remaining: 12.8ms
87:	learn: 0.0982469	total: 86.1ms	remaining: 11.7ms
88:	learn: 0.0968327	total: 86.5ms	remaining: 10.7ms
89:	learn: 0.0953617	total: 86.9ms	remaining: 9.66ms
90:	learn: 0.0941489	total: 87.2ms	remaining: 8.63ms
91:	learn: 0.0932054	total: 87.6ms	remaining: 7.62ms
92:	learn: 0.0920791	total: 88.1ms	remaining: 6.63ms
93:	learn: 0.0910071	total: 88.4ms	remaining: 5.64ms
94:	learn: 0.0896883	total: 88.8ms	remaining: 4.67ms
95:	learn: 0.0884174	total: 89.2ms	remaining: 3.71ms
96:	learn: 0.0873079	total: 89.6ms	remaining: 2.77ms
97:	learn: 0.0865905	total: 90ms	remaining: 1.83ms
98:	learn: 0.0856569	total: 90.4ms	remaining: 912us
99:	learn: 0.0847351	total: 90.8ms	remaining: 0us
 Results from Grid Search 

 The best estimator across ALL searched params:
 

 The best score across ALL searched params:
 0.9443438334960408

 The best parameters across ALL searched params:
 {'depth': 6, 'iterations': 100, 'learning_rate': 0.05}



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