AI Platform Models Cookbook
k-Nearest Neighbors Regression
Initializing search
GAmaranathaReddy/ai-learning
AI Platform Models Cookbook
GAmaranathaReddy/ai-learning
Main Page
Python
Python
keywords
datatypes
classes
functions
numpy
object orientation
interview
Statistics
Maths
ML
ML
Introduction
Prerequisites
Prerequisites
Linear Algebra
Statistics
Types of Data
Classification
Classification
Introduction
Logistic Regression
K-Nearest Neighbours
Support Vector Machines
Naive Bayes
Decision Tree Classification
Random Forest Classification
Stochastic Gradient Descent Classifier (SGD)
Gaussian Process Classification (GPC)
Gradient Boosting Classifier
AdaBoost Classifier
Bagging Classifier
Extra Trees Classifier
Passive Aggressive Classifier
Ridge Classifier
Regression
Regression
Introduction
Linear Regression
Logistic Regression
Polynomial Regression
Stepwise Regression
Ridge Regression
Lasso Regression
ElasticNet Regression
Support Vector Regression
Decision Tree Regression
Random Forest Regression
Gradient Boosting & AdaBoost
XGBoost Regression
Bayesian Linear Regression
Generalized Linear Model (GLM)
Poisson Regression
Negative Binomial Regression
Cox Regression
Multivariate Adaptive Regression Splines (MARS)
Robust Regression
Principal Components Regression (PCR)
Partial Least Squares (PLS) Regression
Tweedie Regression
Quantile Regression
Neural Network Regression
Support Vector Regression
Stochastic Gradient Descent Regression
k-Nearest Neighbors Regression
LightGBM Regression
CatBoost Regression
Clustering
Clustering
Introduction
K-Means Clustering
Hierarchical Clustering
DBSCAN
Mean Shift
OPTICS (Ordering Points To Identify the Clustering Structure)
Affinity Propagation
Spectral Clustering
Agglomerative Clustering
BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies)
Dimensionality Reduction
Dimensionality Reduction
Introduction
Principal Component Analysis
Model Selection
Model Selection
Introduction
Ensemble Learning
Ensemble Learning
Introduction
Natural Language Processing
Natural Language Processing
Introduction
Time Series
Time Series
Introduction
Features
Deep Learning
Reinforcement Learning
Appendix
k-Nearest Neighbors Regression