About the Algorythm Recipe 🍰
Support Vector Machines (SVM) are powerful supervised learning models used for classification and regression tasks. They work by finding the optimal hyperplane that separates different classes or fits the data points in the best possible way. SVMs are particularly effective in high-dimensional spaces and are widely used in various fields such as image classification, text classification, bioinformatics, and financial analysis. They are favored for their ability to handle complex datasets and generalize well to unseen data.
Cookin' time! 🍳
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
# Load a sample dataset (e.g., the Iris dataset)
iris = datasets.load_iris()
X = iris.data
y = iris.target
# For simplicity, let's consider only the first two features and classes
X = X[:, :2]
y = (y != 0) * 1 # Convert target to binary (0 or 1)
# Split the data 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)
# Initialize the SVM classifier
svm_classifier = SVC(kernel='linear')
# Train the SVM classifier
svm_classifier.fit(X_train, y_train)
# Make predictions on the test set
y_pred = svm_classifier.predict(X_test)
# Calculate accuracy
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
STEPS:
We load the Iris dataset from scikit-learn.
We consider only the first two features and two classes for binary classification.
The data is split into training and testing sets.
We initialize an SVM classifier with a linear kernel.
The classifier is trained on the training data.
Predictions are made on the test data.
Finally, we calculate the accuracy of the classifier.
This is a basic example, and there are many parameters you can tune to potentially improve the performance of the SVM classifier, such as the choice of kernel, regularization parameter (C), and others. Additionally, you might want to explore more advanced techniques like cross-validation for hyperparameter tuning.