Overfiting And Underfitting In Machine Learning Data Science

By

Overfitting

Overfitting occurs when a machine learning model tries to fit the training data too closely. It learns even the smallest details, including noise or irrelevant patterns in the training data. As a result, the overfitted model performs very well on the training data but poorly on new or real test data because it cannot generalize.


1. Example of Overfitting

Imagine you are a teacher preparing a student for an exam. You focus so much on last year’s exam questions that the student memorizes them word for word. Now, if new questions appear in the actual exam, the student struggles to answer them correctly because they were only trained on the specific old questions.




This is what overfitting is in machine learning. The model gets too focused on the details and noise of the training data, making it unable to generalize well to new, unseen data.



---


Underfitting


Underfitting happens when the model fails to learn enough from the training data. This means it cannot fully understand the patterns in the training data, leading to poor performance on both the training and new test data. Underfitting typically occurs when the model is too simple or lacks sufficient features.





2. Example of Underfitting


Now, consider another student you’re preparing, but this time, you only teach them basic concepts and give them very few practice questions. As a result, the student is not fully prepared to answer the questions in the exam, especially the more complex ones, because they didn't learn enough.




Thus, to avoid overfitting and underfitting, it is essential to train the model with the right amount of data and balance its complexity so that it performs well on both the training data and new unseen data.


Comments

Post a Comment

"What’s your favorite part of this post? Let us know in the comments!"

Popular posts from this blog

Feature Engineering in Machine Learning: A Beginner's Guide Missing value imputation, handling categorical data ,outlier detection and feature scaling

By
Image
Feature Engineering in Machine Learning: A Beginner's Guide Feature engineering is one of the most critical aspects of machine learning and data science. It involves preparing raw data, transforming it into meaningful features, and optimizing it for use in machine learning models. Simply put, it’s all about making your data as informative and useful as possible. In this article, we’re going to focus on feature transformation, a specific type of feature engineering. We’ll cover its types in detail, including: 1. Missing Value Imputation 2. Handling Categorical Data 3. Outlier Detection 4. Feature Scaling Each topic will be explained in a simple and beginner-friendly way, followed by Python code examples so you can implement these techniques in your projects. What is Feature Transformation? Feature transformation is the process of modifying or optimizing features in a dataset. Why? Because raw data isn’t always machine-learning-friendly. For example: Missing data can confuse your mod...
Read more

Handling Missing Numerical Data with Simple Imputer

By
Image
  Handling Missing Numerical Data with Simple Imputer The SimpleImputer class from sklearn.impute is a common and effective way to handle missing numerical data. It allows you to replace missing values with specific strategies, such as the mean, median, mode, or a constant value. Here's everything you need to know: Why We Use Simple Imputer 1. Clean Data: Machine learning algorithms cannot process missing values. Filling in missing data ensures the dataset is complete. 2. Consistency: Avoid errors caused by missing data. 3. Improved Model Accuracy: Handling missing data correctly can lead to better model performance. 4. Automation: Automates the process of filling missing values in large datasets. Where Simple Imputer Works 1. Numerical Features: Best suited for filling gaps in numeric datasets. 2. Machine Learning Pipelines: Commonly used in preprocessing stages. 3. Real-World Applications: Healthcare: Impute missing blood pressure or glucose readings. Finance: Fill missing custom...
Read more

Feature Construction and Feature Splitting in Machine Learning data science

By
Image
 Feature Construction and Feature Splitting in Machine Learning Feature engineering is one of the most important parts of the machine learning process. It helps improve the performance of models by modifying and optimizing the data. In this article, we’ll focus on two crucial feature engineering techniques: feature construction and feature splitting. Both are beginner-friendly and can significantly improve the quality of your dataset. Let’s break it down into a simple, list-based structure to make it easy to follow. 1. Feature Construction What is Feature Construction? Feature construction is the process of creating new features from raw data or combining existing ones to provide better insights for a machine learning model. For example: From a Date column, we can construct features like Year, Month, or Day. For a Price column, you can create a Price_Per_Unit feature by dividing the price by the number of units. Why is Feature Construction Important? 1. Improves Accuracy: New featu...
Read more