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## AdaBoost in Python - ML From Scratch 13

In this Machine Learning from Scratch Tutorial, we are going to implement the AdaBoost algorithm using only built-in Python modules and numpy. AdaBoost is an ensemble technique that attempts to create a strong classifier from a number of weak classifiers. We will first learn about the concept and the math behind this popular ML algorithm, and then we jump to the code.

All algorithms from this course can be found on GitHub together with example tests.

## Implementation

``````import numpy as np

# Decision stump used as weak classifier
class DecisionStump():
def __init__(self):
self.polarity = 1
self.feature_idx = None
self.threshold = None
self.alpha = None

def predict(self, X):
n_samples = X.shape
X_column = X[:, self.feature_idx]
predictions = np.ones(n_samples)
if self.polarity == 1:
predictions[X_column < self.threshold] = -1
else:
predictions[X_column > self.threshold] = -1

return predictions

def __init__(self, n_clf=5):
self.n_clf = n_clf

def fit(self, X, y):
n_samples, n_features = X.shape

# Initialize weights to 1/N
w = np.full(n_samples, (1 / n_samples))

self.clfs = []
# Iterate through classifiers
for _ in range(self.n_clf):
clf = DecisionStump()

min_error = float('inf')
# greedy search to find best threshold and feature
for feature_i in range(n_features):
X_column = X[:, feature_i]
thresholds = np.unique(X_column)

for threshold in thresholds:
# predict with polarity 1
p = 1
predictions = np.ones(n_samples)
predictions[X_column < threshold] = -1

# Error = sum of weights of misclassified samples
misclassified = w[y != predictions]
error = sum(misclassified)

if error > 0.5:
error = 1 - error
p = -1

# store the best configuration
if error < min_error:
clf.polarity = p
clf.threshold = threshold
clf.feature_idx = feature_i
min_error = error

# calculate alpha
EPS = 1e-10
clf.alpha = 0.5 * np.log((1.0 - min_error + EPS) / (min_error + EPS))

# calculate predictions and update weights
predictions = clf.predict(X)

w *= np.exp(-clf.alpha * y * predictions)
# Normalize to one
w /= np.sum(w)

# Save classifier
self.clfs.append(clf)

def predict(self, X):
clf_preds = [clf.alpha * clf.predict(X) for clf in self.clfs]
y_pred = np.sum(clf_preds, axis=0)
y_pred = np.sign(y_pred)

return y_pred
``````

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