🕶️ A curated list of resources around the topic: visual regression testing

# 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`.

# Logistic Regression with Titanic Dataset In statistics, the logistic model (or logit model) is used to model the probability of a certain class or event existing such as pass/fail, win/lose, alive/dead or healthy/sick. Logistic regression doesn’t predict the continuous values. Logistic regression predicts whether something is True or False. When the probability is grater or equal than 0.5, binary value is 1, when the probability is less than 0.5, binary value is 0. So, the person I just mentioned above will be classify as 1, alive. Model returns 1 (True). https://machinelearningmastery.com/logistic-regression-for-machine-learning/ SVM, I’ll begin by using a linear kernel before using the Gaussian kernel (or the RBF kernel). A Support Vector Machine (SVM) is a machine learning algorithm that is commonly used for classification problems. It’s based on the idea of finding a hyperplane that divides the dataset into classes. The support vectors are the points nearest the hyperplane – these are the critical points within the dataset. ## 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 data has been split into two groups: training set (train.csv) test set (test.csv) The training set should be used to build your machine learning models. For the training set, we provide the outcome (also known as the “ground truth”) for each passenger. Your model will be based on “features” like passengers’ gender and class. You can also use feature engineering to create new features. The test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each passenger. It is your job to predict these outcomes. For each passenger in the test set, use the model you trained to predict whether or not they survived the sinking of the Titanic ## 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`.

🕶️ A curated list of resources around the topic: visual regression testing

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