underfitting

Underfitting and overfitting are critical concepts in machine learning, particularly when using Polynomial Regression to model data. Polynomial regression allows a model to learn non-linear relationships by increasing the polynomial degree (e.g. ), making it highly susceptible to both underfitting (too simple) and overfitting (too complex).Solver

Dropout in Deep Learning

Fraud detection over twitter feed data

A series of documented Jupyter notebooks implementing polynomial regression models and model performance analysis

A visual example of the concepts of under and overfitting in supervised machine learning using U.S. state border data.

Implementation of Decision Tree and Random Forest algorithms, with various hyperparameters, developed from scratch and using scikit-learn for comparison and analysis.

Adding noise as regularization method to reduce overffiting in neural networks

Python version of Andrew Ng's Machine Learning Course.

Evaluating classifier using Python focus on evaluation metrics and hyperparameter turning

A scikit-learn lab comparing classification, regression, underfitting, overfitting, and common evaluation metrics.

Xinshao Wang, Ex-Postdoc and Ex-Visit Scholar@University of Oxford, Ex-Senior Researcher@ZenithAI

Overfitting is often caused by using a model with too many parameters or if the model is too powerful for the given dataset. On the other hand, underfitting is often caused by the model with too few parameters or by using a model that is not powerful enough for the given dataset. In this we are discussing about that.

Brief study on Underfitting and Overfitting in Machine Learning

A repository documenting the work for the Special Course with Professor Søren Hauberg from DTU Cognitive Systems.

Overfitting and Underfitting in Machine Learning

Supervised Learning - Regression Algorithm

IMP KEYS OF ML MODEL

Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.

Make use of PyTorch's custom modules to define a network architecture and train a model. Investigate how to improve a model's performance and deploy your model for wider use.