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# How to optimise learning rates in XGBoost example 2?

# How to optimise learning rates in XGBoost example 2?

This recipe helps you optimise learning rates in XGBoost example 2

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.

So this recipe is a short example of how can optimise multiple parameters in XGBoost.

```
from sklearn import datasets
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import StratifiedKFold
import numpy
```

Here we have imported various modules like numpy, test_train_split, datasets, XGBClassifier 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.

Here we have used datasets to load the inbuilt wine dataset and we have created objects X and y to store the data and the target value respectively.
```
dataset = datasets.load_wine()
X = dataset.data
y = dataset.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
```

```
model = XGBClassifier()
```

In XGBClassifier we want to optimise learning rate by GridSearchCV. So we have set the parameter as a list of values form which GridSearchCV will select the best value of parameter.
```
n_estimators = [100, 200, 300, 400, 500]
learning_rate = [0.0001, 0.001, 0.01, 0.1]
param_grid = dict(learning_rate=learning_rate, n_estimators=n_estimators)
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=7)
```

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: It signifies the number of splits in cross validation.

```
grid_search = GridSearchCV(model, param_grid, scoring="neg_log_loss", n_jobs=-1, cv=kfold)
grid_result = grid_search.fit(X, y)
```

Now we are using print statements to print the results. It will give the values of hyperparameter as a result.
```
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means = grid_result.cv_results_["mean_test_score"]
stds = grid_result.cv_results_["std_test_score"]
params = grid_result.cv_results_["params"]
for mean, stdev, param in zip(means, stds, params):
print("%f (%f) with: %r" % (mean, stdev, param))
```

As an output we get:
Best: -0.077744 using {"learning_rate": 0.1, "n_estimators": 200} -1.086577 (0.000539) with: {"learning_rate": 0.0001, "n_estimators": 100} -1.074744 (0.001073) with: {"learning_rate": 0.0001, "n_estimators": 200} -1.063105 (0.001605) with: {"learning_rate": 0.0001, "n_estimators": 300} -1.051657 (0.002128) with: {"learning_rate": 0.0001, "n_estimators": 400} -1.040397 (0.002643) with: {"learning_rate": 0.0001, "n_estimators": 500} -0.986714 (0.005129) with: {"learning_rate": 0.001, "n_estimators": 100} -0.891275 (0.009533) with: {"learning_rate": 0.001, "n_estimators": 200} -0.808661 (0.013511) with: {"learning_rate": 0.001, "n_estimators": 300} -0.736632 (0.016334) with: {"learning_rate": 0.001, "n_estimators": 400} -0.673480 (0.018467) with: {"learning_rate": 0.001, "n_estimators": 500} -0.443098 (0.032668) with: {"learning_rate": 0.01, "n_estimators": 100} -0.236960 (0.048792) with: {"learning_rate": 0.01, "n_estimators": 200} -0.159834 (0.052822) with: {"learning_rate": 0.01, "n_estimators": 300} -0.125159 (0.056987) with: {"learning_rate": 0.01, "n_estimators": 400} -0.108292 (0.059112) with: {"learning_rate": 0.01, "n_estimators": 500} -0.083225 (0.059937) with: {"learning_rate": 0.1, "n_estimators": 100} -0.077744 (0.057482) with: {"learning_rate": 0.1, "n_estimators": 200} -0.077754 (0.057472) with: {"learning_rate": 0.1, "n_estimators": 300} -0.077754 (0.057472) with: {"learning_rate": 0.1, "n_estimators": 400} -0.077754 (0.057472) with: {"learning_rate": 0.1, "n_estimators": 500}

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