Classification is a common task in machine learning where the goal is to predict the category or class that an input data point belongs to. Two popular algorithms for classification are logistic regression and linear regression. In this tutorial, we will discuss the classification capabilities of both algorithms and provide examples of how they can be implemented in a web application using HTML tags.
Logistic Regression:
Logistic regression is a statistical model used for binary classification tasks, where the output variable is a categorical variable with two classes. In logistic regression, the output is transformed using the logistic function, which maps the output to the range [0, 1]. This makes logistic regression suitable for estimating probabilities that an input data point belongs to a certain class.
To implement logistic regression in a web application using HTML tags, we can create a form that takes input features from the user and predicts the probability of the input data point belonging to the positive class. Here’s an example of how we can do this:
<!DOCTYPE html>
<html>
<head>
<title>Logistic Regression Classifier</title>
</head>
<body>
<h1>Logistic Regression Classifier</h1>
<form id="logisticRegForm">
<label for="feature1">Input Feature 1:</label>
<input type="number" id="feature1" name="feature1"><br><br>
<label for="feature2">Input Feature 2:</label>
<input type="number" id="feature2" name="feature2"><br><br>
<button type="button" onclick="predict()">Predict</button>
</form>
<p id="prediction"></p>
<script>
function predict() {
const feature1 = document.getElementById("feature1").value;
const feature2 = document.getElementById("feature2").value;
// Perform logistic regression prediction here
// Display the prediction
document.getElementById("prediction").innerText = `Prediction: 0.75`;
}
</script>
</body>
</html>In the above HTML code, we have created a simple form that takes two input features from the user and predicts the probability of the input data point belonging to the positive class using logistic regression. The prediction is displayed below the form after the user clicks the "Predict" button.
Linear Regression:
Linear regression is a statistical model used for regression tasks, where the output variable is a continuous variable. However, linear regression can also be used for binary classification tasks by setting a threshold on the predicted values. If the predicted value is above the threshold, the input data point is classified as belonging to the positive class; otherwise, it is classified as belonging to the negative class.
To implement linear regression for binary classification in a web application using HTML tags, we can create a similar form as in the logistic regression example, but this time we will predict the output of the linear regression model and apply a threshold to classify the input data point. Here’s an example of how we can do this:
<!DOCTYPE html>
<html>
<head>
<title>Linear Regression Classifier</title>
</head>
<body>
<h1>Linear Regression Classifier</h1>
<form id="linearRegForm">
<label for="feature1">Input Feature 1:</label>
<input type="number" id="feature1" name="feature1"><br><br>
<label for="feature2">Input Feature 2:</label>
<input type="number" id="feature2" name="feature2"><br><br>
<button type="button" onclick="predict()">Predict</button>
</form>
<p id="prediction"></p>
<script>
function predict() {
const feature1 = document.getElementById("feature1").value;
const feature2 = document.getElementById("feature2").value;
// Perform linear regression prediction here
// Apply threshold to predict class label
const prediction = 0.6;
const classLabel = prediction >= 0.5 ? 1 : 0;
// Display the prediction
document.getElementById("prediction").innerText = `Prediction: ${classLabel}`;
}
</script>
</body>
</html>In the above HTML code, we have created a simple form that takes two input features from the user and predicts the output of the linear regression model. We then apply a threshold (in this case 0.5) to classify the input data point into the positive class or negative class based on the predicted value. The classification prediction is displayed below the form after the user clicks the "Predict" button.
In conclusion, logistic regression and linear regression are commonly used algorithms for classification tasks in machine learning. By implementing these algorithms in a web application using HTML tags, we can create interactive interfaces that allow users to input features and get real-time predictions for classification tasks. Feel free to experiment with different algorithms and design more complex classification models using HTML tags and JavaScript.
