# Iris Dataset with DecisionTree vs Random Forest Decision tree models are the simplest form of tree-based models, and are arguably the simplest form of supervised multivariate classification models. A series of logical tests (generally in the form of boolean comparisons) are applied to the sample entries and their resulting subsets in turn to arrive at a final decision. It is very easy to visualize the decision process in a simple flowchart to trace the rational of every assignment made by a decision tree model, making it among the most interpretable of models. Decision Tree Algorithm in machine learning Random Forest Random forest is an ensemble and supervised machine learning algorithm which is capable of performing both regression and classification problems. Ensemble learning: To form a strong prediction model we join different or same types of algorithms multiple time. Random forest consists of many decision trees. It is kind of forming forest of trees. Means random forest combine multiple same type of decision tree algorithm to form a random forest algorithm. ## Acknowledgements - [Awesome Readme Templates](https://awesomeopensource.com/project/elangosundar/awesome-README-templates) - [Awesome README](https://github.com/matiassingers/awesome-readme) - [How to write a Good readme](https://bulldogjob.com/news/449-how-to-write-a-good-readme-for-your-github-project) ## Appendix The Iris dataset was used in R.A. Fisher's classic 1936 paper, The Use of Multiple Measurements in Taxonomic Problems, and can also be found on the UCI Machine Learning Repository. It includes three iris species with 50 samples each as well as some properties about each flower. One flower species is linearly separable from the other two, but the other two are not linearly separable from each other. The columns in this dataset are: Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species ## Authors - [@nitinkumar388](https://github.com/nitinkumar388) ## Badges Add badges from somewhere like: [shields.io](https://shields.io/) [](https://github.com/tterb/atomic-design-ui/blob/master/LICENSEs) [](https://opensource.org/licenses/) [](http://www.gnu.org/licenses/agpl-3.0) ## Contributing Contributions are always welcome! See `contributing.md` for ways to get started. Please adhere to this project's `code of conduct`.