What is Flask?
Flask is a lightweight Python web framework that allows developers to build web applications quickly and easily. It provides a minimal set of tools and libraries needed to create web applications, making it a popular choice for small to medium-sized projects.
How does Flask work?
Flask works by handling incoming HTTP requests and returning appropriate HTTP responses. It uses a simple routing system to map URL patterns to Python functions, which are responsible for handling the request, processing data, and generating the response. Flask also supports template rendering, request handling, and various extensions for adding more functionality, such as database integration, authentication, and form handling.
Example of a simple Flask application:
from flask import Flask, render_template
app = Flask(__name__)
@app.route("/")
def home():
return render_template("index.html")
if __name__ == "__main__":
app.run(debug=True)
In this example, a Flask application is created, and a route is defined for the root URL ("/"). The home function is called when a user visits the root URL, and it renders an HTML template called “index.html”.
Resources for learning Flask:
title: GridSearchCV date: 2023-05-04 description: ‘GridSearchCV is a technique for hyperparameter tuning in machine learning models. It is a part of the scikit-learn library in Python and stands for “Grid Search Cross-Validation.” GridSearchCV systematically searches through a specified range of hyperparameter values, evaluates the model performance using cross-validation, and selects the best combination of hyperparameters that results in the highest cross-validated score.’
What is GridSearchCV?
GridSearchCV is a technique for hyperparameter tuning in machine learning models. It is a part of the scikit-learn library in Python and stands for “Grid Search Cross-Validation.” GridSearchCV systematically searches through a specified range of hyperparameter values, evaluates the model performance using cross-validation, and selects the best combination of hyperparameters that results in the highest cross-validated score.
GridSearchCV example in Python:
from sklearn.datasets import load_iris
from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV, train_test_split
# Load the iris dataset
data = load_iris()
X, y = data.data, data.target
# 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)
# Define the hyperparameter search space
param_grid = {'C': [0.1, 1, 10, 100], 'kernel': ['linear', 'rbf'], 'gamma': ['auto', 'scale']}
# Create the GridSearchCV object
grid_search = GridSearchCV(SVC(), param_grid, cv=5, scoring='accuracy')
# Fit the model on the training data
grid_search.fit(X_train, y_train)
# Print the best combination of hyperparameters and the corresponding score
print("Best parameters: ", grid_search.best_params_)
print("Best cross-validated score: ", grid_search.best_score_)
In this example, we use GridSearchCV to tune the hyperparameters of a Support Vector Machine (SVM) classifier on the Iris dataset.
