Welcome to the Logistic Regression tutorial using TensorFlow! This guide will walk you through building a binary classification model using TensorFlow's powerful APIs. Whether you're a beginner or looking to refine your skills, you'll find practical examples and explanations here.

📚 What is Logistic Regression?

Logistic Regression is a statistical method used for binary classification problems. Unlike linear regression, it predicts the probability of a class using the sigmoid function. It's widely applied in machine learning for tasks like spam detection and medical diagnosis.

📌 Tip: For a deeper dive into machine learning fundamentals, check out our Machine Learning Basics tutorial.

🧰 Step-by-Step Implementation

1. Install TensorFlow

Ensure TensorFlow is installed in your environment:

pip install tensorflow

2. Import Libraries

import tensorflow as tf
from tensorflow.keras import layers, models
import numpy as np
import matplotlib.pyplot as plt

3. Prepare the Dataset

Use a simple dataset like the Iris dataset for demonstration:

# Load dataset (example)
data = np.random.rand(100, 2)  # Random data for simplicity
labels = (data[:, 0] + data[:, 1] > 1).astype(int)  # Binary labels

4. Build the Model

Create a basic logistic regression model:

model = models.Sequential([
    layers.Dense(1, activation='sigmoid', input_shape=(2,))
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

5. Train the Model

Train the model with your dataset:

history = model.fit(data, labels, epochs=100, validation_split=0.2)

6. Evaluate Results

Visualize training progress:

Training Process
```python plt.plot(history.history['accuracy'], label='accuracy') plt.plot(history.history['val_accuracy'], label='val_accuracy') plt.legend() plt.show() ```

📈 Example Output

After training, your model will output probabilities. For instance:

  • Input: [0.5, 0.7] → Output: 0.85 (class 1)
  • Input: [0.2, 0.3] → Output: 0.30 (class 0)

Use the sigmoid function to map predictions to probabilities: $$ \sigma(z) = \frac{1}{1 + e^{-z}} $$

📚 Expand Your Knowledge

📌 Notes

  • Always normalize your data for better convergence.
  • Use cross-validation to assess model generalization.
  • For multi-class problems, consider softmax activation instead of sigmoid.

Let me know if you need further clarification! 🚀