Hello, and welcome to our journey into the AdaBoost algorithm! AdaBoost, an abbreviation for Adaptive Boosting, is a crucial ensemble learning method employed in machine learning. Using Python, we'll build an AdaBoost model from scratch and learn how to boost prediction accuracy by combining multiple weak learners into a powerful one.

First, let's define our terms. Boosting is a technique in which several weak learners are combined to create a strong learner, thereby improving our predictive model. AdaBoost largely follows the same principle. However, it introduces an important twist: it adapts by focusing more on instances that were incorrectly predicted in previous iterations by assigning them higher weights.

Consider a multiphase bank loan approval process to illustrate this concept. Each phase in this process acts as a weak learner. The first phase might be a credit score check, followed by an employment history verification in the second phase, and so on. Collectively, these weak learners form a strong learner who decide on loan approval.

Now, let's bring AdaBoost to life with Python.

We begin by initializing the AdaBoost class, specifying the parameters (including the number of learners and the learning rate), and initializing lists to store the models and their weights:

import numpy as np
from sklearn.datasets import make_classification
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier

class AdaBoost:
    def __init__(self, num_learners=10, learning_rate=1):
        self.num_learners = num_learners
        self.learning_rate = learning_rate
        self.models = []
        self.model_weights = []