Iris
Different ML Techniques on the Iris dataset. For more information, see this Wikipedia article.
Data
About the data
Iris dataset is a multivariate data set with 150 samples and 4 features. The data set is named after the iris plant. The data set is loaded from this link. It comes pre-installed with the Python package sklearn. We have obtained the data set from this Kaggle link.
Features
The data set contains the following features:
| Feature | Description |
|---|---|
| Id | The id of the sample |
| SepalLengthCm | the length of the sepals |
| SepalWidthCm | the width of the sepals |
| PetalLengthCm | the length of the petals |
| PetalWidthCm | the width of the petals |
| Species | the species of the iris plant |
ML Techniques
Tentative ML techniques: - K-Nearest Neighbors - Logistic Regression - Decision Tree - Random Forest - Naive Bayes - SVM - K-Means - Linear Discriminant Analysis - Quadratic Discriminant Analysis - Gaussian Process - Gradient Boosting - Bagging - AdaBoost - Extra Trees - Voting Classifier - Stacking Classifier - Bagging Classifier - Extra Trees Classifier - Gradient Boosting Classifier - Gaussian Process Classifier - Random Forest Classifier - Voting - Stacking - Logistic Regression - Perceptron - Passive-Aggressive - Ridge - SGD - SVC - Linear SVC - NuSVC - One-Class SVM