underfitting
There are 22 repositories under underfitting topic.
dr-mushtaq/Machine-Learning
A complete A-Z guide to Machine Learning and Data Science using Python. Includes implementation of ML algorithms, statistical methods, and feature selection techniques in Jupyter Notebooks. Follow Coursesteach for tutorials and updates.
tazriahelal/Dropout_Regularization-
Dropout in Deep Learning
codeKk21/Classification-Optimization-technique
Fraud detection over twitter feed data
jamesdhope/regression-models
A series of documented Jupyter notebooks implementing polynomial regression models and model performance analysis
raffal88/Machine-learning
Python version of Andrew Ng's Machine Learning Course.
alejandrods/Noise-Regularization-Method-Neural-Network
Adding noise as regularization method to reduce overffiting in neural networks
fardinabbasi/Decision_Tree
Implementation of Decision Tree and Random Forest algorithms, with various hyperparameters, developed from scratch and using scikit-learn for comparison and analysis.
jackyhuynh/EvaluationMetric
Evaluating classifier using Python focus on evaluation metrics and hyperparameter turning
vc1492a/us-state-under-over-fitting
A visual example of the concepts of under and overfitting in supervised machine learning using U.S. state border data.
XinshaoAmosWang/XinshaoAmosWang.github.io
Xinshao Wang, Ex-Postdoc and Ex-Visit Scholar@University of Oxford, Ex-Senior Researcher@ZenithAI
AVINASH-KURREY/IMPORTANT-KEYS-ON-ML-MODEL
IMP KEYS OF ML MODEL
d4rthm4ul/ML-Overfitting-Lasso-and-Ridge-Regression
In this repository you will learn how to handle overfitting with the help of Lasso and Ridge Regression regularizations, also working mechanism of those while using useful charts.
sciencenerd880/regression_polynomial
Supervised Learning - Regression Algorithm
778569/Model-overfitting-and-Underfitting
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.
AnuragSChatterjee/Introduction-To-Machine-Learning-In-Python-On-Kaggle
Pursued an Introductory Machine Learning course in Python on Kaggle in my free time, where I practiced on a dataset and built a small model on Kaggle.
Develop-Packt/Creating-Ensemble-Models-with-Python
Recognize underfitting and overfitting, implement bagging and boosting, and build a stacked ensemble model using a number of classifiers.
Develop-Packt/Solving-a-Classification-Problem-with-DNNs-Using-PyTorch
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.
kmock930/Machine-Learning-for-Images
This project helps exercising my machine learning modelling skills and evaluation techniques on image-based models.
miguelangelnieto/Predicting-Boston-Housing-Prices
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.
Ninad077/Machine_Learning-Gradient_descent_with_Logistic_Regression
Content: Classification, Sigmoid function, Decision Boundary, Cost function, Gradient descent, Overfitting, Regularisation
sivapanuganti/Underfitting-and-Overfitting
Brief study on Underfitting and Overfitting in Machine Learning
SRafi007/Overfitting-and-Underfitting-in-Machine-Learning
Overfitting and Underfitting in Machine Learning