Data Science Dojo

AUGUST 19, 2024

This happens when the model is too simple to capture the underlying patterns in the data. To mitigate overfitting and underfitting: Regularization: Techniques like L1 and L2 regularization can help prevent overfitting by penalizing complex models. Technical Skills Implement a simple linear regression model from scratch.

AI 363

AI AI Machine Learning Machine Learning 363

Pickl AI

JUNE 4, 2024

Summary : Building a machine learning model is just one step. Validating its performance on unseen data is crucial. Python offers various tools like train-test split and cross-validation to assess model generalizability. This helps identify overfitting and select the best model for real-world use.

Cross Validation 52

Cross Validation Python Machine Learning Machine Learning 52

Pickl AI

NOVEMBER 28, 2024

Decision Trees These trees split data into branches based on feature values, providing clear decision rules. Model Evaluation and Tuning After building a Machine Learning model, it is crucial to evaluate its performance to ensure it generalises well to new, unseen data.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Mlearning.ai

NOVEMBER 29, 2023

GP has intrinsic advantages in data modeling, given its construction in the framework of Bayesian hierarchical modeling and no requirement for a priori information of function forms in Bayesian reference. Decision Trees ML-based decision trees are used to classify items (products) in the database.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

DagsHub

APRIL 21, 2024

It offers implementations of various machine learning algorithms, including linear and logistic regression , decision trees , random forests , support vector machines , clustering algorithms , and more. There is no licensing cost for Scikit-learn, you can create and use different ML models with Scikit-learn for free.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Dataconomy

SEPTEMBER 1, 2023

Hybrid machine learning techniques excel in model selection by amalgamating the strengths of multiple models. By combining, for example, a decision tree with a support vector machine (SVM), these hybrid models leverage the interpretability of decision trees and the robustness of SVMs to yield superior predictions in medicine.

Decision Trees 195

Decision Trees Machine Learning Machine Learning Support Vector Machines 195