4Geeks logo
4Geeks logo

Courses

Explore our extensive collection of courses designed to help you master various subjects and skills. Whether you're a beginner or an advanced learner, there's something here for everyone.

Coding Bootcamp

Learn live

Join us for our free workshops, webinars, and other events to learn more about our programs and get started on your journey to becoming a developer.

Upcoming live events

Learning library

For all the self-taught geeks out there, here is our content library with most of the learning materials we have produced throughout the years.

It makes sense to start learning by reading and watching videos about fundamentals and how things work.

Data Science and Machine Learning - 16 wks

Full-Stack Software Developer - 16w

Search from all Lessons

LoginGet Started
← Back to Lessons
Edit on Github
Open in Collab

Exploring Boosting Algorithm

Boosting in Python

Next we will see how we can implement this model in Python. To do this, we will use the xgboost library.

Boosting for classification

To exemplify the implementation of a boosting algorithm for classification we will use the same data set as in the case of decision trees and random forest.

Step 1. Reading the processed dataset

In [1]:
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

X, y = load_iris(return_X_y = True, as_frame = True)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)

X_train.head()
Out[1]:
sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)
224.63.61.00.2
155.74.41.50.4
656.73.14.41.4
114.83.41.60.2
424.43.21.30.2

The train set will be used to train the model, while the test set will be used to evaluate the effectiveness of the model. In addition, the predictor variables do not need to be normalized, since the decision trees that make up the XGBoost models are not affected by the scale of the data because of the way they work: they make decisions based on certain feature thresholds, regardless of their scale.

However, if models other than decision trees are added for boosting, data standardization is necessary.

Step 2: Initialization and training of the model

In [2]:
from xgboost import XGBClassifier

model = XGBClassifier(random_state = 42)
model.fit(X_train, y_train)
Out[2]:
XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              objective='multi:softprob', predictor=None, ...)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

The training time of a model will depend, first of all, on the size of the dataset (instances and features), and also on the model type and its configuration.

Step 3: Model prediction

Once the model has been trained, it can be used to predict with the test sample of the dataset.

In [3]:
y_pred = model.predict(X_test)
y_pred
Out[3]:
array([1, 0, 2, 1, 1, 0, 1, 2, 1, 1, 2, 0, 0, 0, 0, 1, 2, 1, 1, 2, 0, 2,
       0, 2, 2, 2, 2, 2, 0, 0])

With raw data it is very difficult to know whether the model is getting it right or not. To do this, we must compare it with reality. There are a large number of metrics to measure the effectiveness of a model in predicting, including accuracy, which is the fraction of predictions that the model made correctly.

In [4]:
from sklearn.metrics import accuracy_score

accuracy_score(y_test, y_pred)
Out[4]:
1.0

The model is perfect!

Step 4: Saving the model

Once we have the model we were looking for (presumably after hyperparameter optimization), in order to be able to use it in the future it is necessary to store it in our directory, together with the seed.

In [5]:
model.save_model("xgb_classifier_default_42.json")

Adding an explanatory name to the model is vital, since in the case of losing the code that has generated it we will know, on the one hand, what configuration it has (in this case we say default because we have not customized any of the hyperparameters of the model, we have left those that the function has by default) and also the seed to replicate the random components of the model, which in this case we do it adding a number to the file name, the 42.

Boosting for regression

To exemplify the implementation of a boosting algorithm for regression we will use the same data set as in the case of decision trees and random forests.

Step 1. Reading the processed dataset

In [6]:
import pandas as pd

train_data = pd.read_csv("https://raw.githubusercontent.com/4GeeksAcademy/machine-learning-content/master/assets/clean_petrol_consumption_train.csv")
test_data = pd.read_csv("https://raw.githubusercontent.com/4GeeksAcademy/machine-learning-content/master/assets/clean_petrol_consumption_test.csv")

train_data.head()
Out[6]:
Petrol_taxAverage_incomePaved_HighwaysPopulation_Driver_licence(%)Petrol_Consumption
08.0444785770.529464
17.5487023510.529414
28.05319118680.451344
37.0434539050.672968
47.5335741210.547628
In [7]:
X_train = train_data.drop(["Petrol_Consumption"], axis = 1)
y_train = train_data["Petrol_Consumption"]
X_test = test_data.drop(["Petrol_Consumption"], axis = 1)
y_test = test_data["Petrol_Consumption"]

The train set will be used to train the model, while the test set will be used to evaluate the effectiveness of the model. In addition, the predictor variables do not need to be normalized, since the decision trees that make up the XGBoost models are not affected by the scale of the data because of the way they work: they make decisions based on certain feature thresholds, regardless of their scale.

However, if models other than decision trees are added for boosting, data standardization is necessary.

Step 2: Initialization and training of the model

In [8]:
from xgboost import XGBRegressor

model = XGBRegressor(random_state = 42)
model.fit(X_train, y_train)
Out[8]:
XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=None, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=None, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             n_estimators=100, n_jobs=None, num_parallel_tree=None,
             predictor=None, random_state=42, ...)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Step 3: Model prediction

Once the model has been trained, it can be used to predict with the test sample of the dataset.

In [9]:
y_pred = model.predict(X_test)
y_pred
Out[9]:
array([577.82245, 551.888  , 602.6116 , 616.01685, 490.87717, 613.6035 ,
       560.9523 , 932.38385, 552.1893 , 647.02783], dtype=float32)

To calculate the effectiveness of the model we will use the mean squared error (MSE):

In [10]:
from sklearn.metrics import mean_squared_error

print(f"Mean squared error: {mean_squared_error(y_test, y_pred)}")
Mean squared error: 12803.160311029944

Step 4: Saving the model

Once we have the model we were looking for (presumably after hyperparameter optimization), in order to be able to use it in the future it is necessary to store it in our directory, together with the seed.

In [11]:
model.save_model("xgb_regressor_default_42.json")

Adding an explanatory name to the model is vital, since in the case of losing the code that has generated it we will know, on the one hand, what configuration it has (in this case we say default because we have not customized any of the hyperparameters of the model, we have left those that the function has by default) and also the seed to replicate the random components of the model, which in this case we do it adding a number to the file name, the 42.