What is overfitting in machine learning?

   Quality Thought – The Best Data Science Training in Hyderabad

Looking for the best Data Science training in Hyderabad? Quality Thought offers industry-focused Data Science training designed to help professionals and freshers master machine learning, AI, big data analytics, and data visualization. Our expert-led course provides hands-on training with real-world projects, ensuring you gain in-depth knowledge of Python, R, SQL, statistics, and advanced analytics techniques.

Why Choose Quality Thought for Data Science Training?

✅ Expert Trainers with real-time industry experience
✅ Hands-on Training with live projects and case studies
✅ Comprehensive Curriculum covering Python, ML, Deep Learning, and AI
✅ 100% Placement Assistance with top IT companies
✅ Flexible Learning – Classroom & Online Training

Supervised and Unsupervised Learning are two primary types of machine learning, differing mainly in  The primary goal of a data science project is to extract actionable insights from data to support better decision-making, predictions, or automation—ultimately solving a specific business or real-world problem. 

Overfitting in machine learning occurs when a model learns the training data too well, including its noise and random fluctuations, instead of capturing the underlying patterns. As a result, the model performs very well on training data but poorly on new, unseen data.

---

🔹 Key Points About Overfitting

1. Symptoms

   • High accuracy on training data.

   • Low accuracy or high error on validation/test data.

   • Model is overly complex relative to the dataset size.

2. Causes

   • Model is too complex (e.g., too many parameters or deep neural network layers).

   • Insufficient training data.

   • Noise in the training data is learned as “patterns.”

   • Too many training epochs without regularization.

3. Example

   • Suppose you fit a polynomial of degree 10 to 10 data points. The curve might pass through every point perfectly (low training error) but wildly fluctuate between points (high test error).

---

🔹 How to Prevent Overfitting

Technique	Description
Train/Test Split	Evaluate the model on unseen data to detect overfitting early.
Cross-Validation	Use k-fold cross-validation to ensure stability across datasets.
Regularization	Add penalties (L1, L2) to discourage overly complex models.
Pruning	Reduce the complexity of decision trees or neural networks.
Early Stopping	Stop training when validation performance stops improving.
Dropout	Randomly drop neurons in neural networks during training.
More Data / Data Augmentation	Provide the model with more varied examples to generalize better.

---

✅ In short: Overfitting is when a model memorizes the training data instead of learning general patterns, leading to poor performance on new data.

I can also draw a visual comparison of underfitting, proper fitting, and overfitting—it’s a classic diagram in machine learning. Do you want me to create it?

Read More

Explain feature engineering in data.

Visit QUALITY THOUGHT Training Institute in Hyderabad

Get Direction