By Saeed Mirshekari

Sep 4, 2023

Understanding Overfitting in Machine Learning

Overfitting is a term frequently encountered in the world of data science and machine learning. It's a concept that can make or break the success of your predictive models. In this comprehensive guide, we'll delve deep into what overfitting is, why it's detrimental, and how to combat it. Whether you're a budding data scientist or an enthusiast, this article will equip you with the knowledge to tackle overfitting head-on.

Table of Contents

  • What is Overfitting?
  • Why is Overfitting Bad?
  • Detecting Overfitting
  • Mitigating Overfitting
  • Cross-Validation
  • Regularization Techniques
  • Conclusion

What is Overfitting?

At its core, overfitting is an issue that arises when a machine learning model captures not only the underlying patterns in the data but also the noise and random fluctuations. In simpler terms, it's when your model becomes too complex and starts fitting the training data too closely. This leads to poor generalization, meaning that the model performs well on the training data but poorly on new, unseen data.

Let's illustrate this concept with an example. Imagine you're building a model to predict the price of houses based on their square footage. You collect data on various houses, fit a model to it, and achieve great results. However, if your model is too complex, it might start capturing peculiarities specific to your training data, such as a single house with an unusually low price due to unique circumstances. This would make your model less useful when you try to predict house prices for a different dataset.

To visualize overfitting, let's create a simple polynomial regression example:

import numpy as np
import matplotlib.pyplot as plt

# Generate some random data
X = np.random.rand(20, 1)
y = 2 * X + 1 + 0.1 * np.random.randn(20, 1)

# Fit a linear regression model
from sklearn.linear_model import LinearRegression
model = LinearRegression(), y)

# Plot the data and the model's predictions
plt.scatter(X, y, label='Data')
plt.plot(X, model.predict(X), color='red', label='Linear Regression')
plt.title('Linear Regression Fit')

In this case, the linear regression model fits the data quite well, capturing the underlying relationship between X and y. But what if we make the model overly complex?

from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import PolynomialFeatures

# Create a polynomial regression model
degree = 15
polyreg = make_pipeline(PolynomialFeatures(degree), LinearRegression()), y)

# Plot the data and the polynomial regression model's predictions
plt.scatter(X, y, label='Data')
plt.plot(X, polyreg.predict(X), color='green', label='Polynomial Regression')
plt.title('Polynomial Regression Fit (Overfit)')

As you can see, the polynomial regression model with a degree of 15 fits the training data exceptionally well, but it does so by introducing unnecessary complexity. This complexity makes it prone to overfitting when applied to new, unseen data.

Why is Overfitting Bad?

Understanding why overfitting is bad is crucial because it highlights the importance of addressing this issue in your machine learning projects. Here are some key reasons why overfitting is detrimental:

1. Poor Generalization

Overfit models do not generalize well to new, unseen data. In real-world scenarios, you often want your machine learning models to make accurate predictions on data they haven't encountered before. If your model is overfit, it will perform poorly on this new data because it has essentially memorized the training data, including its noise and outliers.

2. Wasted Resources

Training an overfit model can be computationally expensive and time-consuming. It's like memorizing a textbook instead of understanding the concepts. You end up wasting computational resources on unnecessary details.

3. Misleading Insights

Overfit models can lead to misleading insights. When you analyze the model's coefficients or feature importances, you might draw incorrect conclusions about the importance of certain features. These conclusions could lead to poor decision-making in real-world applications.

4. Unreliable Predictions

In business and research, unreliable predictions can have severe consequences. For example, in healthcare, an overfit model predicting patient outcomes might lead to incorrect treatment decisions. In finance, it can result in significant financial losses.

5. Reduced Model Robustness

Overfit models are often highly sensitive to small changes in the training data. This lack of robustness can be problematic when dealing with noisy or incomplete datasets.

Now that we understand the perils of overfitting let's explore methods for detecting it in our models.

Detecting Overfitting

Detecting overfitting is a critical step in the machine learning pipeline. Fortunately, there are several methods and techniques to help you identify when your model is overfitting:

1. Holdout Validation

Holdout validation involves splitting your dataset into two subsets: a training set and a validation set. You train your model on the training set and evaluate its performance on the validation set. If your model performs significantly better on the training set than on the validation set, it's likely overfitting.

Here's how you can implement holdout validation in Python:

from sklearn.model_selection import train_test_split

X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)

# Train your model on X_train and y_train, then evaluate on X_val and y_val.

2. Cross-Validation

Cross-validation is an extension of holdout validation that involves dividing the data into multiple subsets (folds) and training and evaluating the model multiple times. This helps provide a more robust estimate of your model's performance. Common cross-validation techniques include k-fold cross-validation and stratified cross-validation.

from sklearn.model_selection import cross_val_score

# Define your model (e.g., model = LinearRegression())
scores = cross_val_score(model, X, y, cv=5)  # 5-fold cross-validation

If the performance of your model varies significantly across different folds, it could indicate overfitting.

3. Learning Curves

Learning curves are plots that show the model's performance on the training and validation sets as a function of the number of training examples. If your model overfits, you'll typically see a large gap between the training and validation performance curves. Learning curves can help you identify whether collecting more data would be beneficial or if your model is too complex.

from sklearn.model_selection import learning_curve

# Create learning curves
train_sizes, train_scores, val_scores = learning_curve(model, X, y, cv=5)

By analyzing these learning curves, you can gain insights into whether your model is overfitting or underfitting.

Mitigating Overfitting

Now that we can identify overfitting, let's explore various techniques to mitigate it and build more reliable machine learning models.


Cross-validation, as

mentioned earlier, not only helps detect overfitting but also provides an effective way to mitigate it. By assessing your model's performance on multiple validation sets, you can get a more robust estimate of its generalization performance. You can also experiment with different hyperparameters and model architectures during cross-validation to find the best model that balances bias and variance.

Regularization Techniques

Regularization is a set of techniques used to prevent overfitting by adding constraints or penalties to the model's parameters during training. Two common regularization techniques are L1 (Lasso) and L2 (Ridge) regularization:

L1 Regularization (Lasso)

L1 regularization adds the absolute values of the model's coefficients as a penalty term to the loss function. This encourages the model to select only the most important features and push the less relevant ones toward zero.

from sklearn.linear_model import Lasso

lasso_model = Lasso(alpha=0.01)  # Alpha controls the strength of regularization, y)

L2 Regularization (Ridge)

L2 regularization adds the square of the model's coefficients as a penalty term to the loss function. This encourages the model to have small, evenly distributed coefficients.

from sklearn.linear_model import Ridge

ridge_model = Ridge(alpha=0.01)  # Alpha controls the strength of regularization, y)

Both L1 and L2 regularization techniques can help combat overfitting by preventing the model from fitting the training data too closely.

Feature Selection

Another approach to mitigate overfitting is feature selection. Removing irrelevant or redundant features from your dataset can reduce the complexity of your model and make it less prone to overfitting. You can use techniques like feature importance scores, recursive feature elimination, or domain knowledge to identify and select the most informative features.

from sklearn.feature_selection import SelectFromModel

# Select features based on their importance
selector = SelectFromModel(lasso_model, prefit=True)
X_new = selector.transform(X)

Increase Training Data

One of the most effective ways to combat overfitting is to increase the size of your training dataset. More data can help the model generalize better, as it has more examples to learn from. If collecting more data is feasible, it's often a highly recommended strategy.

Simplify the Model

If none of the above techniques prove sufficient, consider simplifying your model architecture. You can reduce the number of hidden layers and neurons in a neural network, decrease the polynomial degree in polynomial regression, or choose a simpler algorithm altogether. Simpler models are less likely to overfit.


In the world of data science and machine learning, understanding overfitting and its consequences is essential. Overfitting is when a model becomes too complex, fitting the training data too closely and resulting in poor generalization to new data. It's bad because it leads to unreliable predictions, wasted resources, and misleading insights.

To combat overfitting, you can use techniques such as cross-validation, regularization, feature selection, increasing training data, and simplifying the model. By implementing these strategies, you can build more robust and reliable machine learning models that perform well not only on the training data but also in real-world scenarios.

Remember that overfitting is a common challenge, and the key to success in machine learning lies in finding the right balance between bias and variance. Continuously monitoring and refining your models will help you navigate the complex landscape of data science and make meaningful predictions.

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About O'Fallon Labs

In O'Fallon Labs we help recent graduates and professionals to get started and thrive in their Data Science careers via 1:1 mentoring and more.

Saeed Mirshekari, PhD

Saeed is currently a Director of Data Science in Mastercard and the Founder & Director of O'Fallon Labs LLC. He is a former research scholar at LIGO team (Physics Nobel Prize of 2017). Learn more about Saeed...

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