How to use Adaboost Classifier and Regressor in Python?
MACHINE LEARNING RECIPES DATA CLEANING PYTHON DATA MUNGING PANDAS CHEATSHEET     ALL TAGS

How to use Adaboost Classifier and Regressor in Python?

How to use Adaboost Classifier and Regressor in Python?

This recipe helps you use Adaboost Classifier and Regressor in Python

0
In [1]:
## How to use Adaboost Classifier and Regressor in Python
def Snippet_162():
    print()
    print(format('How to use Adaboost Classifier and Regressor in Python','*^82'))

    import warnings
    warnings.filterwarnings("ignore")

    # load libraries
    from sklearn import datasets
    from sklearn import metrics
    from sklearn.ensemble import AdaBoostClassifier
    from sklearn.ensemble import AdaBoostRegressor
    from sklearn.model_selection import train_test_split
    import matplotlib.pyplot as plt
    import seaborn as sns

    plt.style.use('ggplot')

    # load the iris datasets
    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=0.25)

    # fit a CART model to the data
    model = AdaBoostClassifier()
    model.fit(X_train, y_train)
    print(); print(model)

    # make predictions
    expected_y  = y_test
    predicted_y = model.predict(X_test)

    # summarize the fit of the model
    print(); print(metrics.classification_report(expected_y, predicted_y))
    print(); print(metrics.confusion_matrix(expected_y, predicted_y))

    # load the boston datasets
    dataset = datasets.load_boston()
    X = dataset.data; y = dataset.target
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

    # fit a CART model to the data
    model = AdaBoostRegressor()
    model.fit(X_train, y_train)
    print(); print(model)

    # make predictions
    expected_y  = y_test
    predicted_y = model.predict(X_test)

    # summarize the fit of the model
    print(); print(metrics.r2_score(expected_y, predicted_y))
    print(); print(metrics.mean_squared_log_error(expected_y, predicted_y))

    # plot regression
    plt.figure(figsize=(10,10))
    sns.regplot(expected_y, predicted_y, fit_reg=True, scatter_kws={"s": 100})

Snippet_162()
**************How to use Adaboost Classifier and Regressor in Python**************

AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
          learning_rate=1.0, n_estimators=50, random_state=None)

              precision    recall  f1-score   support

           0       1.00      1.00      1.00        16
           1       0.60      1.00      0.75         6
           2       1.00      0.75      0.86        16

   micro avg       0.89      0.89      0.89        38
   macro avg       0.87      0.92      0.87        38
weighted avg       0.94      0.89      0.90        38


[[16  0  0]
 [ 0  6  0]
 [ 0  4 12]]

AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear',
         n_estimators=50, random_state=None)

0.8109522137559658

0.034124635515305146

Relevant Projects

Sequence Classification with LSTM RNN in Python with Keras
In this project, we are going to work on Sequence to Sequence Prediction using IMDB Movie Review Dataset​ using Keras in Python.

Human Activity Recognition Using Smartphones Data Set
In this deep learning project, you will build a classification system where to precisely identify human fitness activities.

Choosing the right Time Series Forecasting Methods
There are different time series forecasting methods to forecast stock price, demand etc. In this machine learning project, you will learn to determine which forecasting method to be used when and how to apply with time series forecasting example.

Data Science Project - Instacart Market Basket Analysis
Data Science Project - Build a recommendation engine which will predict the products to be purchased by an Instacart consumer again.

Identifying Product Bundles from Sales Data Using R Language
In this data science project in R, we are going to talk about subjective segmentation which is a clustering technique to find out product bundles in sales data.

Mercari Price Suggestion Challenge Data Science Project
Data Science Project in Python- Build a machine learning algorithm that automatically suggests the right product prices.

Data Science Project in Python on BigMart Sales Prediction
The goal of this data science project is to build a predictive model and find out the sales of each product at a given Big Mart store.

Predict Employee Computer Access Needs in Python
Data Science Project in Python- Given his or her job role, predict employee access needs using amazon employee database.

Predict Macro Economic Trends using Kaggle Financial Dataset
In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.

Music Recommendation System Project using Python and R
Machine Learning Project - Work with KKBOX's Music Recommendation System dataset to build the best music recommendation engine.