Data Science Dojo

JULY 15, 2024

decision trees, support vector regression) that can model even more intricate relationships between features and the target variable. Decision Trees: These work by asking a series of yes/no questions based on data features to classify data points. converting text to numerical features) is crucial for model performance.

Machine Learning 307

Machine Learning Machine Learning Algorithm Clustering 307

Mlearning.ai

FEBRUARY 15, 2023

Some important things that were considered during these selections were: Random Forest : The ultimate feature importance in a Random forest is the average of all decision tree feature importance. A random forest is an ensemble classifier that makes predictions using a variety of decision trees.

Cross Validation 52

Cross Validation Decision Trees Algorithm Natural Language Processing 52

Pickl AI

JULY 26, 2023

Here are some examples of variance in machine learning: Overfitting in Decision Trees Decision trees can exhibit high variance if they are allowed to grow too deep, capturing noise and outliers in the training data.

Machine Learning 52

Machine Learning Machine Learning Cross Validation Decision Trees 52

Towards AI

JULY 19, 2023

(Check out the previous post to get a primer on the terms used) Outline Dealing with Class Imbalance Choosing a Machine Learning model Measures of Performance Data Preparation Stratified k-fold Cross-Validation Model Building Consolidating Results 1. among supervised models and k-nearest neighbors, DBSCAN, etc.,

Machine Learning 40

Machine Learning Machine Learning K-nearest Neighbors Support Vector Machines 40

Mlearning.ai

NOVEMBER 29, 2023

K-Nearest Neighbor Regression Neural Network (KNN) The k-nearest neighbor (k-NN) algorithm is one of the most popular non-parametric approaches used for classification, and it has been extended to regression. 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

Pickl AI

NOVEMBER 18, 2024

For example, linear regression is typically used to predict continuous variables, while decision trees are great for classification and regression tasks. Decision trees are easy to interpret but prone to overfitting. predicting house prices), Linear Regression, Decision Trees, or Random Forests could be good choices.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Algorithm 52

Pickl AI

SEPTEMBER 12, 2024

Cross-Validation: A model evaluation technique that assesses how well a model will generalise to an independent dataset. Decision Trees: A supervised learning algorithm that creates a tree-like model of decisions and their possible consequences, used for both classification and regression tasks.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52