Understanding AdaBoost is crucial for building robust and accurate predictive models. But what exactly is AdaBoost? AdaBoost, short for Adaptive Boosting, is an ensemble learning technique that combines multiple classifiers to form a strong classifier. Think of it as a team of experts working together, where each expert focuses on their areas where others might have made mistakes. We fit a classifier on the data and evaluate overall errors. The error used for calculating the weight should be given for that classifier in the final additive model evaluation. The intuitive sense is that higher weight will be given to the model with fewer errors. Finally, weights for each observation will be updated. Here, the weight will be increased for incorrectly classified observations in order to give more focus to the next iterations, and weights will be reduced for correctly classified observations. The weak classifiers are combined with their respective weights to form a strong classifier. This diagram gives us a quick idea on how weights changed in the last iteration compared with the initial iteration. So now, let's see the working of the AdaBoost algorithm. This image illustrates an example of the AdaBoost algorithm using the dataset provided. Okay, so this is a classification problem since the target column is binary. Initially, each data point will be assigned an equal weight, as you can see, 1x5. The sample weights are calculated using the following formula, and here, n denotes the total number of data points. In Step 2, we will first assess how well gender classifies the samples, followed by evaluation of how the variables age and income perform in classifying the samples. For each feature, we will create a decision stump and calculate its Gini index. The tree with the lowest Gini index will be selected as the first system. Let’s assume that, in our dataset, gender has the lowest Gini index, making it our first stump. In Step 3, using this approach, we will now determine the amount of say, or importance, or influence for this classifier and categorize the data points using this formula. Here, the total error is just the sum of misclassified data point sample weights. If there is one incorrect output in our dataset, thus our total error is 1x5, and the alpha performance of this stump. Now, we will find out the performance of the stump. So, using this formula, here, zero represents a flawless stump, while one represents a bad stump. According to this graph, where zero is misclassification, there is no error, hence the amount of say (Alpha) you can say will be a huge value. Okay, when the classifier predicts half correctly and half incorrectly, the total error is 0.5, and the classifier’s significant amount of say equals 0. And if all the samples were improperly categorized, the error will be quite large, about 1, and our Alpha value will be a negative integer. Okay, negative integer you can say minus two. Okay, in step four, you might be wondering why it's necessary to calculate the stump's total error and performance. The reason is straightforward: we need to update the weights because if the same weights are used in the next model, it will yield the same result as the previous one. The weights of incorrectly predicted points will be increased, while those of the correctly predicted points will be decreased. Okay, more emphasis will be placed on the points with higher weights. So, after assessing the classifier's significance and the total error, we update the weights using this formula: New sample weight = Old weight * amount of say (Alpha). When the sample is successfully identified, the amount of say (Alpha) will be negative, and when the sample is misclassified, the amount of say (Alpha) will be positive. You can see here the plus and minus. There are four correctly categorized samples and one incorrectly classified sample. So, in this case, the sample weight of the data point is 1x5, and the quantity of say (performance of the Gentle stump) is 0.69. Okay, so these are the following new sample weights. Okay, so this is the adjusted weight for incorrectly categorized points: 0.398. So, here the total sum of the weights must equal 1, but the updated weights add up to 0.84. To normalize them, we divide each weight by 0.84, making the total sum equal to one. After normalization, the data weights now sum to one. Okay, see, now the sum is one. So, in step five, we must know how to create a fresh dataset to see whether or not the mistakes have decreased. To do this, we will delete the same and new sample weights column and then split our data points into buckets based on the new sample weights. Okay, and in Step six, we are almost done here. The method now selects random values between 0 and 1. Since misclassified records have higher sample weights, they are more likely to be selected. Let's say the algorithm randomly selects the number 0.38, 0.26, or anything else. Next, we will see where these random numbers fall within the weighted range and create our new dataset. So, this is our new dataset. In Step seven, the new dataset will be used to repeat the previous steps. Start by assigning equal weights to each point, identifying the term, the best classifier for the sample, calculating the Gini index, and selecting the one with the lowest value. So, assume we have sequentially built three decision trees: DT1, DT2, and DT3 using our dataset. Okay, so when we run our dataset through these trees, the class with the majority will be determined, and we will make predictions based on that.