ML Algorithms


Training and Testing In Machine Learning we create models to predict the outcome of certain events, to measure if the model is good enough, we can use a method called Train/Test. Train/Test is a method to measure the accuracy of your model. Regularized Regression Models Linear Regression Bagging and Boosting Logistic Regression Naïve Bayes K-Means Clustering Random Forest Decision Tree
Topic Replies Views Activity
About the ML Algorithms category
ML Algorithms
0 165 September 10, 2021
Recurrent Neural Network vs LSTM
ML Algorithms
3 34 December 30, 2022
Random forest vs Bagging techniques
Random Forest
2 35 December 30, 2022
What is k-fold cross-validation?
Training and Testing
1 55 December 22, 2022
How can you avoid overfitting your model?
Regularized Regression Models
1 48 December 22, 2022
What Is K-Means Clustering?
K-Means Clustering
1 104 December 22, 2022
Why isn't it recommended that you use Simple Linear Regression for Time Series data?
Linear Regression
1 573 May 5, 2019
Coefficients of Random Forest
Random Forest
1 2201 May 5, 2019
How to correct for heteroskedasticity in Linear Regression and coding help?
Linear Regression
1 941 May 4, 2019
Choosing an appropriate Train-Test Split Size
ML Algorithms
3 4181 April 23, 2019
Lasso Regression Vs Ridge Regression
Regularized Regression Models
2 703 April 17, 2019
What does P-value signify about the statistical data?
ML Algorithms
4 705 April 15, 2019
R glmnet as.matrix() error message
Regularized Regression Models
1 7000 December 20, 2017
Overfitting using Random Forest
Random Forest
1 509 April 3, 2019
Step-wise regression using p values to drop variables with non-significant p values
Regularized Regression Models
1 658 April 3, 2019
KMeans Clustering Vs KNearest Neighbors
K-Means Clustering
1 469 March 18, 2019
Purity in Decision Trees: Gini Vs Entropy
Decision Tree
1 1034 March 1, 2019