AdaBoost: Powering Predictive Models Through Adaptive Boosting

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Welcome to the exciting world of AdaBoost! In this article, we’ll explore AdaBoost – a powerful technique that can make your machine-learning models even better. Let’s dive in!

AdaBoost stands for “Adaptive Boosting.” Just like how a rocket boosts a spaceship into space, AdaBoost boosts your machine-learning models to help them perform better. It’s like a team of horses pulling a cart – each horse (or in our case, a ‘weak learner’) contributes to moving the cart forward (making a prediction), and AdaBoost is the coach that guides the team to make sure they’re all pulling in the right direction.

So, when do we use AdaBoost? Imagine you’re playing a game of chess. You don’t just make random moves; you think ahead, planning your moves based on the current state of the board. AdaBoost is similar – it makes decisions by taking into account the past moves (previous models) and then adjusts the current move (current model) accordingly. This makes AdaBoost a valuable tool when you want your machine-learning model to learn from its mistakes and improve over time.

Now that we know what AdaBoost is and when to use it, let’s take a step back and explore the broader context that AdaBoost fits into – the world of classification algorithms and ensemble learning!


Before we dive into AdaBoost, let’s first look at some basic concepts from the world of machine learning that will help us understand AdaBoost better.

Firstly, let’s recap what classification algorithms are. These algorithms help us categorize things. Imagine you’re sorting marbles by color. You put all the red ones together, all the blue ones together, and so on. That’s what a classification algorithm does – it sorts data into different categories (or classes). Some popular classification algorithms are Decision Trees, Naive Bayes, and Logistic Regression.

Next, let’s look at ensemble learning. Remember our team of horses pulling the cart? That’s an example of ensemble learning – multiple models (or ‘weak learners’) working together to make a prediction. Just like how multiple horses can pull a cart faster and smoother than a single horse, multiple models can often make better predictions than a single model. AdaBoost is a type of ensemble learning technique.

Finally, let’s talk about the bias-variance trade-off. Imagine you’re playing darts. If your darts consistently hit to the left of the target, you’re biased (you favor the left). But if your darts are all over the place, you have high variance (you’re inconsistent). In machine learning, we face a similar issue – models can be biased (too simple and miss important trends) or have high variance (too complex and overfitting the training data). AdaBoost helps balance this trade-off to achieve a model that’s just right – not too simple, not too complex.

Now that we’ve covered the basics, let’s deep dive into the world of AdaBoost!


Just like a good coach who trains a team, AdaBoost trains a group of weak learners to make them stronger. Now, you might be wondering, what is a weak learner? Let’s go back to our horses pulling a cart example. Each horse might not be strong enough to pull the cart alone, but when they work together, they can move the cart. In AdaBoost, these ‘horses’ are our weak learners. A weak learner is a model that is slightly better than random guessing. For example, if you are trying to classify images of cats and dogs, a weak learner would be able to identify the correct animal just a little more than half the time.

AdaBoost starts by training a weak learner on the entire dataset. Then, it looks at how well this learner did. If it made any mistakes, AdaBoost pays more attention to those mistakes in the next round of training. It’s like the coach spotting that a horse is lagging and giving it more training so it can pull better next time.

This process is repeated many times, each time creating a new weak learner that focuses on the mistakes of the previous ones. In the end, AdaBoost combines all these weak learners into a single strong model, just like how a team of horses can pull a cart together. And that’s how AdaBoost works!


Now let’s take a closer look at how AdaBoost actually works. Remember how we talked about AdaBoost ‘paying more attention’ to mistakes? This is done through something called ‘weights’.

At the start, all data points in our dataset are given an equal weight. Think of it as all our horses being equally strong. But as we start training our weak learners, some data points are correctly classified, while others are not.

AdaBoost then increases the weights of the incorrectly classified data points and decreases the weights of the correctly classified ones. It’s like our coach noticing which horses are lagging (incorrectly classified data points) and which are doing fine (correctly classified ones), and giving more training (increased weights) to the ones that are lagging.

The next weak learner is then trained with these updated weights. This makes it focus more on the data points that were previously misclassified, just like our coach focusing on the horses that need more training.

This process is repeated for a number of rounds, each time updating the weights based on the mistakes of the current weak learner and then training a new weak learner with these weights.

In the end, AdaBoost combines all the weak learners into a single strong model. This isn’t a simple combination, though. Just like our coach would rely more on the stronger horses to pull the cart, AdaBoost gives more importance to the weak learners that had a better performance. This is done using the weights again – the final model gives more weight to the weak learners that had a lower error rate.

The beauty of AdaBoost lies in its simplicity and effectiveness. By focusing on the mistakes and constantly learning from them, it creates a strong model that can make accurate predictions.

Image Credit: GeeksforGeeks

Now let’s see how we can use AdaBoost in a real-world example!



Imagine you have a bag of candy and you’re trying to guess which color of candy you’ll pull out next. You ask your friends to take turns guessing, and after each guess, you tell them if they’re wrong or right. Some of your friends are really good at guessing, while others aren’t so good. But as they keep guessing and learning from their mistakes, they start to get better and better. That’s basically how AdaBoost works. It’s a machine learning algorithm that combines the “guesses” (predictions) of several “friends” (weak learners) to make a strong prediction. And just like your friends learn from their mistakes, AdaBoost learns from the mistakes of weak learners.

Weak Learners:

Now, let’s talk about these “friends” or “weak learners.” A weak learner is just a machine learning model that is a little better than random guessing. It’s like a friend who can guess the color of the candy correctly just a little more than half the time. AdaBoost starts by training one weak learner on the entire dataset and then, based on its mistakes, trains more weak learners. These weak learners are then combined to create a strong model that can make accurate predictions.

Weight Updates:

Remember how we said AdaBoost learns from the mistakes of the weak learners? This is done through weight updates. Initially, all data points are given equal importance (or weight). But as the weak learners make their predictions, the weights of the data points they got wrong are increased. This makes the next weak learner pay more attention to these points. It’s like saying to your friend, “You guessed red candy last time and were wrong. Now, guess again, but remember, you’re more likely to be wrong if you guess red.” The weight updates help AdaBoost focus on the hard-to-classify points and improve its predictions.

Error Rates:

The error rate is the proportion of incorrect predictions made by a weak learner. It’s like the number of times your friend guessed the wrong color of the candy. AdaBoost uses the error rates to update the weights and to decide how much importance to give to each weak learner when making the final prediction.

Iterative Learning:

Iterative learning is the process of learning from mistakes and improving over time. Each time a weak learner makes a prediction, AdaBoost learns from its mistakes and trains another weak learner. This process is repeated several times, resulting in a strong model that can make accurate predictions. It’s like your friends improving their candy color guessing skills after each round of guesses.


Predicting Customer Churn:

One common use of AdaBoost is in predicting customer churn – which customers are likely to stop doing business with a company. Let’s imagine a telecom company. They have lots of data on their customers – how long they spend on calls, how many texts they send, their data usage, and more. They want to use this data to predict which customers are likely to cancel their service.

Here’s where AdaBoost comes in. The telecom company can use AdaBoost to train several weak learners on this data, each learner focusing on different features (like call duration, number of texts, etc.). Then, AdaBoost combines these weak learners into a strong model that can predict if a customer is likely to churn.

Image Classification:

Another use of AdaBoost is in image classification – determining what’s in an image. For example, let’s say we have a lot of pictures and we want to categorize them into ‘pictures with a dog’ and ‘pictures without a dog’.

AdaBoost can be used here to train several weak learners on the images, each learner focusing on different features (like color, shapes, etc.). Each learner on its own might not be very good at identifying dogs, but when they’re combined, they can do a pretty good job.

Medical Diagnosis:

AdaBoost can also be used for medical diagnosis. Let’s say a hospital has data on patients with a certain disease. The data includes things like age, blood pressure, cholesterol levels, and so on. The hospital wants to use this data to predict which patients are likely to have the disease.

Here’s where AdaBoost comes in. The hospital can use AdaBoost to train several weak learners on this data, each learner focusing on different features (like age, blood pressure, etc.). Then, AdaBoost combines these weak learners into a strong model that can predict if a patient is likely to have the disease.

The beauty of AdaBoost is that it can be used in so many different situations – anywhere you have data and want to make a prediction. And the best part is, even though it’s a powerful algorithm, it’s quite easy to understand and implement!


The dataset we’ll be using for this tutorial is called the “Breast Cancer Wisconsin (Diagnostic)” dataset, often just referred to as the “Breast Cancer” dataset. It’s a very popular dataset for beginners in machine learning because it’s relatively simple, but still provides opportunities for meaningful insights.

This dataset is included in the sklearn datasets module, so it’s easy to access. It consists of 569 samples of malignant and benign tumor cells. The first two columns in the dataset store the unique ID numbers of the samples and the corresponding diagnoses (M = malignant, B = benign), respectively. Columns 3-32 contain 30 real-valued features that have been computed from digitized images of the cell nuclei, which can be used to build the model to predict whether a tumor is malignant or benign.

Each of these features relates to some property of the cell nuclei present in the image, such as the radius of the nucleus, the texture, the perimeter, area, smoothness, compactness, concavity, concave points, symmetry, and fractal dimension. The mean, standard error, and “worst” or largest (mean of the three worst/largest values) of these features were computed for each image, resulting in 30 features.

For simplicity and better understanding, we are going to use only the mean values of these features (first 10 features) for our model.


Now let’s get into the practical part of this tutorial where we’ll apply the AdaBoost algorithm to our dataset. I’ll guide you step-by-step through the process.

First, let’s import the necessary packages:

# Importing necessary libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.ensemble import AdaBoostClassifier
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.preprocessing import LabelEncoder

Next, let’s load our dataset:

# Load the dataset
cancer = datasets.load_breast_cancer()

# Let's convert to dataframe for better visualization and manipulation
df = pd.DataFrame(data= np.c_[cancer['data'], cancer['target']],
                  columns= list(cancer['feature_names']) + ['target'])

Now, let’s prepare our data:

# Selecting first 10 features for simplicity
selected_features = df.columns[:10]

# Separating features (X) and target (y)
X = df[selected_features]
y = df['target']

# Splitting the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

We can now proceed to train our AdaBoost model:

#Create AdaBoost classifier
adaboost = AdaBoostClassifier(n_estimators=50, learning_rate=1, random_state=42)

# Train AdaBoost classifier, y_train)

Now, let’s use our trained model to make predictions:

# Making predictions
y_pred = adaboost.predict(X_test)

Let’s evaluate our model using a confusion matrix and classification report:

# Create confusion matrix
conf_matrix = confusion_matrix(y_test, y_pred)

# Visualize the confusion matrix
sns.heatmap(conf_matrix, annot=True, fmt='d',

After creating and visualizing the confusion matrix, we can generate a classification report to see the precision, recall, f1-score, and support of our model:

# Generate classification report
class_report = classification_report(y_test, y_pred)

The classification report will give us an overview of how well our model is performing. The precision will tell us the proportion of positive identifications that were actually correct. The recall will tell us the proportion of actual positives that were identified correctly. The F1 score is a function of precision and recall, and it’s a way to summarize the evaluation of the model into a single number.

Next, we can also evaluate the model by checking its accuracy:

# Calculate accuracy
accuracy = adaboost.score(X_test, y_test)
print("Model accuracy: ", accuracy)


This concludes the AdaBoost tutorial. Check out the following Google Bolab File:


Before we jump into the numbers, let’s take a moment to understand what we’re looking at. We’ve got two main sets of data: the Classification Report and the Confusion Matrix. Both of these are tools that help us understand how well our AdaBoost model is working.

Let’s start with the Classification Report:

  • Accuracy: The accuracy of our model is approximately 0.974 (or 97.4% if you prefer percentages). That means our AdaBoost model is correctly predicting whether a tumor is benign or malignant about 97.4% of the time. That’s pretty good!
  • Precision: Precision tells us how often our model is right when it predicts a certain class. For example, when our model predicts that a tumor is benign (class 0.0), it is right 98% of the time. When it predicts that a tumor is malignant (class 1.0), it is right 97% of the time.
  • Recall: Recall tells us how often our model correctly identifies a certain class. For example, of all the actual benign tumors (class 0.0), our model correctly identifies 95% of them. Of all the actual malignant tumors (class 1.0), it correctly identifies 99% of them.
  • F1-Score: The F1-Score is a way of combining precision and recall into a single number. It’s like the average of precision and recall. But unlike a regular average, it gives more weight to lower numbers. This means a high F1-Score is only possible if both precision and recall are high. For our model, the F1-Score is 0.96 for benign tumors and 0.98 for malignant tumors.

Now let’s move on to the Confusion Matrix:

Confusion Matrix

This matrix is a table that shows us how often our model is right and how often it’s wrong. It’s called a “confusion” matrix because it shows where our model is getting “confused.”

  • The top left number (41) is the number of benign tumors (class 0.0) that our model correctly identified. This is good!
  • The bottom right number (70) is the number of malignant tumors (class 1.0) that our model correctly identified. This is also good!
  • The top right number (2) is the number of benign tumors that our model incorrectly thought were malignant. This is a mistake.
  • The bottom left number (1) is the number of malignant tumors that our model incorrectly thought were benign. This is also a mistake.

So overall, our AdaBoost model is doing a pretty good job. But there’s always room for improvement!


When you’re choosing a machine learning algorithm, it’s a bit like choosing a tool from a toolbox. There isn’t one tool that’s the best at everything – it depends on what you’re trying to do. Let’s compare AdaBoost with some other popular classification algorithms to help you decide which tool is best for your problem.

Image Credit: Towards Data Science

Decision Tree: Imagine you’re playing a game of 20 questions. You’re trying to guess what object your friend is thinking of, and you can only ask yes-or-no questions. That’s basically what a decision tree does. It asks a series of yes-or-no questions about the data (like “Is the number of calls greater than 100?”) to make a prediction.

AdaBoost, on the other hand, is like a team of players playing 20 questions. Each player asks their own set of questions, and their answers are combined to make the final prediction.

Both AdaBoost and decision trees are easy to understand and interpret. But AdaBoost often gives better predictions because it combines the answers from multiple “players.”

Random Forest: A random forest is like a decision tree but with a twist. Instead of one player asking all the questions, a random forest has multiple players (or “trees”) each asking a different set of questions. This can give more accurate predictions than a single decision tree.

But AdaBoost takes this idea even further. Instead of just having multiple players, it also learns from their mistakes. So over time, the “team” of players in AdaBoost can become more and more skilled at the game.

Support Vector Machines (SVM): Imagine you’re trying to separate two groups of points on a graph. You could draw a line between them, but there might be many lines that could work. SVM finds the line that has the maximum distance from the points of both groups, making it a robust choice for many classification problems.

AdaBoost, on the other hand, doesn’t just draw one line. It combines multiple “weak” lines (drawn by the weak learners) to create a “strong” boundary that separates the groups. So while SVM focuses on the “best” line, AdaBoost combines many “good enough” lines to make a great prediction.

Gradient Boosting Machine (GBM), LightGBM, XGBoost, and CatBoost: These are all variations of gradient boosting, which is another type of ensemble learning method like AdaBoost. The main difference is in how they learn from mistakes. While AdaBoost adjusts the weights of the data points that are hard to predict, gradient-boosting methods try to fit the new predictor to the residual errors made by the previous predictor.

LightGBM, XGBoost, and CatBoost are more advanced versions of GBM. They offer more sophisticated ways of dealing with categorical variables, missing data, and large datasets. However, they can be more complex and harder to understand than AdaBoost.

Remember, all these algorithms have their own strengths and weaknesses. The choice of algorithm depends on the specific problem you’re trying to solve, the nature of your data, and the trade-off between prediction accuracy and model interpretability. That’s why it’s important to understand the data you’re working with and to experiment with different algorithms to see which one works best.

One way to compare these algorithms is to apply them to the same dataset and compare the results. Let’s say we use the same Breast Cancer dataset that we used for AdaBoost. We preprocess the data in the same way and apply each of the above algorithms separately.

Once we have the results, we can compare them in a few ways:

  1. Accuracy: This is the percentage of correct predictions. The higher the accuracy, the better the model. However, accuracy alone isn’t always a good indicator of a model’s performance. It doesn’t tell us how the model performs for each class of data.
  1. F1-Score: As we mentioned earlier, the F1-Score is a combination of precision and recall. It’s a good way to compare the performance of models, especially when the classes are imbalanced.
  1. Confusion Matrix: This gives a detailed view of how the model performs for each class. We can compare the number of true positives, true negatives, false positives, and false negatives for each model.
  1. Time: Another important factor to consider is how long it takes for each algorithm to train. Some algorithms like SVM can take a long time to train on large datasets, while others like AdaBoost and Decision Trees are usually faster.
  1. Interpretability: Finally, we need to consider how easy it is to understand the model. Simple models like Decision Trees and AdaBoost are usually easier to interpret than more complex models like SVM or Gradient Boosting. This might be important if you need to explain your model to non-technical stakeholders.

After comparing these factors, you might find that one model performs better than the others on this particular dataset. But remember, this doesn’t mean it will perform better on all datasets. It’s always important to test different algorithms on your specific problem and data.

In terms of the maths behind these algorithms, each one has its own unique mathematical foundation:

  • Decision Trees use concepts from information theory like entropy and information gain.
  • Random Forests use the same concepts as Decision Trees but add a layer of randomness to improve model diversity.
  • SVM uses the principles of optimization and vector spaces to find the optimal boundary between classes.
  • Gradient Boosting Methods (including GBM, LightGBM, XGBoost, and CatBoost) use the principles of gradient descent, a numerical optimization technique.


Before we start, let’s remember that no algorithm is perfect and each one has its strengths and weaknesses. Let’s explore some of these for AdaBoost.

Advantages of AdaBoost:

  1. Easy to Understand: Just like our candy guessing game, AdaBoost is simple to understand. It works by combining the efforts of several weak learners (our friends) to create a strong prediction (guess the candy color).
  2. Effective: AdaBoost is often very effective and can provide accurate predictions, whether it’s guessing the color of the candy, predicting customer churn, classifying images, or diagnosing diseases.
  3. Not Prone to Overfitting: AdaBoost is less likely to overfit or perform too well on the training data but poorly on unseen data. This is because it focuses on the hard-to-classify points and doesn’t just memorize the data.
  4. Versatile: AdaBoost can be used with different types of data and for various tasks, such as classification and regression.

Limitations of AdaBoost:

  1. Sensitive to Noisy Data and Outliers: AdaBoost can struggle if the data is noisy (has irrelevant or misleading features) or has outliers (data points that are very different from the rest). This is because it gives more weight to the hard-to-classify points, which can be misleading if these points are noisy or outliers.
  2. Time and Computation: AdaBoost can be time-consuming and require a lot of computational power, especially if there are many weak learners or if the dataset is large. This is because it trains each weak learner sequentially (one after the other).
  3. Choice of Weak Learner: The performance of AdaBoost also depends on the choice of the weak learner. If the weak learner is too complex, AdaBoost can overfit. If the weak learner is too weak, AdaBoost can underfit.


And there you have it! AdaBoost is a powerful tool that, much like a team of friends guessing candy colors, combines the strength of multiple weak learners to make a strong and often accurate prediction. It’s easy to understand, versatile, and often effective, making it a great tool for a wide range of tasks.

However, it’s also important to remember that AdaBoost isn’t perfect. It can be sensitive to noisy data and outliers, can take a lot of time and computation, and its performance depends on the choice of the weak learner. But even with these limitations, it’s a great tool to have in your machine-learning toolkit!

In the following topics in this series, we’ll explore other machine learning algorithms like SVM (Support Vector Machines), SGD (Stochastic Gradient Descent), and QDA (Quadratic Discriminant Analysis). Just like AdaBoost, these algorithms have their strengths and weaknesses and are useful for different kinds of tasks.

See you in the next article!

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