# How to perform AdaBoost classification using skicit-learn Python?

by kanika Updated: Jul 13, 2023

Solution Kit

An AdaBoost is an ensemble learning algorithm. It combines many weak classifiers to create a strong classifier. It is particularly effective in classification tasks. However, it can be extended to regression problems as well. It assigns varying weights to training instances based on their difficulty in classification. It allows the next weak classifiers to focus on the challenging instances.

The choice of the classifier depends on the problem's nature and the data characteristics. Classifiers can be categorized into two types:

#### Linear classification:

It takes a linear relationship between the input features and the class labels. They aim to find a linear decision boundary that separates different classes. Linear Classifiers includes Logistic Regression, Support Vector Machines, and Naive Bayes Classifier.

#### Nonlinear classifiers:

They are designed to handle complex relationships between features and class labels. A linear decision boundary cannot represent it. These classifiers are more flexible and can capture nonlinear patterns in the data. Nonlinear classification includes Decision Trees, Random Forests, Neural Networks, and K-Nearest Neighbors.

Here's a step-by-step breakdown of how the AdaBoost classifier works:

**Step - 1: Initialize weights: **

Each instance in the training set is assigned an initial weight, usually set to equal values. The weights of each instance during the process weighted mean predicted class probabilities.

**Step - 2: Train a weak classifier: **

A weak classifier is often a decision stump (a decision tree with a single split) trained on the training data. The weak classifier's goal is to minimize the weighted error rate. It is where the weights emphasize the instances. They were misclassified in the previous iterations.

**Step - 3: Evaluate the weak classifier: **

The weak classifier's performance is evaluated by calculating its weighted error rate. The error rate is the sum of the misclassified instances divided by the sum of all instance weights. The weaker the classifier, the higher its error rate.

**Step - 4: Adjust instance weights: **

Instances that were misclassification by the weak classifier are assigned higher weights. It gives them more influence in subsequent iterations. Correctly classified instances have their weights reduced. This change focuses the subsequent weak classifiers on the difficult instances.

**Step - 5: Update the classifier weight: **

The weight of the weak classifier is determined based on its performance. The better the classifier's performance, the higher its weight in the final classifier.

**Step - 6: Combine weak classifiers: **

The weak classifiers are combined by assigning weights to their predictions. The weights of the classifiers themselves determine the weights. A strong classifier is created by summing the weighted final predictions.

**Step - 7: Repeat steps 2-6: **

2nd to 6th steps is repeated for a predefined iteration (controlled by the n_estimators). Else it can be until a specified criterion is met. Each iteration focuses on the before misclassified data, improving the classifier's performance.

**Step -8: Make predictions: **

The final strong classifier predicts the class labels of new instances. It aggregates the predicted value, weighted by their respective classifier weights.

AdaBoost algorithm has several advantages:

- It can achieve high accuracy by combining many weak classifiers. The boosting technique focuses on difficult instances, improving performance.
- It is less prone to overfitting compared to some other complex models. The boosting algorithm adjusts the sample weighting and focuses on misclassified instances. It reduces the chances of memorizing the training data.

But, the AdaBoost model also has some limitations:

- It is sensitive to noisy data and outliers, affecting performance.
- It may be expensive. Mainly if many weak classifiers are used or the original dataset is large.

AdaBoost is a powerful and ensemble learning algorithm used in machine learning projects. It boosts the weak classifier's performance, making it effective in various domains. It can be image recognition, text classification, fraud detection, and medical diagnosis.

Here is an example of performing AdaBoost classification using skicit-learn Python.

Fig 1: Preview of the Code.

Fig 2: Preview of the Output.

### Code

In this solution, we are performing AdaBoost classification using skicit-learn Python

### Instructions

__Follow the steps carefully to get the output easily.__

- Install Jupyter Notebook on your computer.
- Open terminal and install the required libraries with following commands.
- Install sklearn by using the command: pip install sklearn.
- Install numpy by using the command: pip install numpy.
- Copy the code using the "Copy" button above and paste it into your IDE's Python file.
- Import the used libraries by following commands.
- import numpy: import numpy as np.
- Run the file.

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.

*I found this code snippet by searching for *"How to perform AdaBoost using scikit-learn using Python?" *in kandi. You can try any such use case!*

### Dependent Libraries

numpyby numpy

The fundamental package for scientific computing with Python.

numpyby numpy

Python 23755 Version:v1.25.0rc1 License: Permissive (BSD-3-Clause)

scikit-learnby scikit-learn

scikit-learn: machine learning in Python

scikit-learnby scikit-learn

Python 54584 Version:1.2.2 License: Permissive (BSD-3-Clause)

If you do not have scikit-learn that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the scikit-learn page in kandi.

*You can search for any dependent library on kandi like *scikit-learn

### Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

- The solution is created in Python 3.9.6
- The solution is tested on numpy version 1.21.4
- The solution is tested on sklearn version 1.1.3

Using this solution, we are able to perform AdaBoost classification using skicit-learn Python

### Support

- For any support on kandi solution kits, please use the chat
- For further learning resources, visit the Open Weaver Community
learning page.

### FAQ:

**1. What are class probabilities in the Adaboost classifier? **

Class probabilities represent the predicted probabilities belonging to a particular class. AdaBoost makes binary classifications and can be extended to handle problems using strategies.

The AdaBoost classifier assigns weights to weak classifiers based on their performance. These weights determine the influence of each weak classifier in the final ensemble. The AdaBoost algorithm combines the predictions of the weak classifiers. It considers their weights and generates a final prediction.

The class probabilities can be obtained by converting the weighted outputs into probabilities. One common approach is to use a weighted majority vote. It is where the weak classifier's weights determine their final prediction. These weights are then normalized to get class probabilities.

**2. How is the weighted mean predicted class probabilities determined using an AdaBoost classifier? **

The weighted mean predicted class probabilities are determined by combining the weak classifiers. It is based on their respective weights. It involves calculating the weighted average of the predicted class probabilities. Here is an explanation of the weighted mean predicted class probabilities are determined:

- Train the AdaBoost classifier.
- Get predicted class probabilities.
- Weight the predictions.
- Combine the weighted predictions.
- Calculate the total weight.
- Compute the weighted mean predicted class probabilities.

**3. Can a boosting algorithm such as AdaBoost solve regression problems? **

Yes, boosting algorithms can solve regression problems in addition to classification tasks. While boosting algorithms, they can be adapted and extended to handle regression problems. The adaptation of boosting algorithms is referred to as gradient boosting for regression. One popular implementation of boosting for regression is the Gradient Boosting Machine (GBM).

In regression boosting, the goal is to build an ensemble of weak regression models. It can predict a continuous target variable. It can combine weak models and focus on the instances with higher errors, still apply.

**4. How does one use AdaBoost in Python for a classification problem? **

To use AdaBoost in Python for a classification problem, you can follow these steps:

- Import the necessary libraries and modules.
- Prepare your dataset. Ensure you have your input features and target labels in separate data structures.
- Separate the dataset into training and testing sets. It helps evaluate the performance of the AdaBoost classifier on unseen data.
- Create an instance of the AdaBoostClassifier class and set any desired parameters. It can customize the weak classifiers (n_estimators) and the learning rate (learning rate).
- Train the AdaBoost classifier on the training data.
- Make predictions on the test data.
- Evaluate the performance of the AdaBoost classifier. Do it by comparing the predicted labels (y_pred) with the true labels (y_test).

**5. What is the learning rate of an AdaBoost algorithm? What effect does it have on the model's performance? **

The learning rate of AdaBoost is a hyperparameter. It controls the contribution of each weak classifier to the final ensemble. It determines how much weight is given to the predictions during the boosting. The parameter learning rate denotes the learning rate. It takes values between 0 and 1. A learning rate of less than 1 reduces the impact of each weak classifier. It is while a learning rate equal to 1 assign equal weight to each weak classifier.

The effect of the learning rate on the model's performance can be summarized as follows:

- Learning rate and ensemble complexity.
- Learning rate and convergence speed.
- Learning rate and robustness to noise.
- Learning rate and overfitting.