These are the source files for a light weight implementation of machine learning algorithms.
The idea is to learn machine learning by implementing the algorithms from ground up, harnessing the capabilities of plain numpy. This initiative goes beyond utilizing existing tools; it is a meticulous crafting of custom implementations that dive deep into the intricate mathematics underpinning each algorithm.
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Using the LinearRegression estimator:
import numpy as np
from sklearn import datasets
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from ai.linear_model import LinearRegression
X, y = datasets.make_regression(n_samples=100, n_features=1, noise=20, random_state=4)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42
)
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
print(accuracy_score(y_pred, y_test))
