Machine Learning and Deep Learning

  Support Vector Machine (SVM): It is a supervised Machine Learning Algorithm. It is used to Classification and Regression model.  What is SVM SVM is a Machine Learning algorithm. It creates the best line or decision boundary (Hyper plane) that can separate out n-dimensional space into classes. The decision boundary is used to classify new data.   SVM  is a type of supervised Learning Algorithm.    It requires labeled data (Input and output both) for training.    It is used to solve classification and regression problems.         When should we use a Support Vector Machine? Support vector machine is used two classify two-dimensional and multidimensional data and also regression model. Decision Boundary: Decision boundary (Line or Plane) plays important role in the classification.                         Figur...

  Creating a Random Forest: Steps Bootstrapped Dataset              The bootstrap method is a resampling technique used to estimate statics on a population by sampling a dataset with Replacement. It can be used to estimate summary statistics such as the mean or standard deviation. The bootstrap dataset (Same size as original) is created by randomly selecting samples from the original dataset. Following is our sample dataset. Fa mily History High BP Overweight Weight (Kg) Diabetes No No No 65 No Yes Yes Yes 100 Yes Yes Yes No 75 No Yes No Yes 110 Yes •       Step 1: Create a Bootstrap Dataset •       Step 2: Creating Decision Tree: Bootst...

  Introduction Random Forest is a widely-used  machine learning algorithm  developed by Leo Breiman and Adele Cutler, which combines the output of multiple decision trees to reach a single result. Its ease of use and flexibility have fueled its adoption, as it handles both classification and regression problems What is a Random Forest Algorithm? Random Forest Algorithm- widespread popularity stems from its user-friendly nature and adaptability, enabling it to effectively tackle classification and regression problems. The algorithm’s strength lies in its ability to handle complex datasets and mitigate overfitting, making it a valuable tool for various predictive tasks in machine learning. One of the most important features of the Random Forest Algorithm is that it can handle the data set containing  continuous variables,  as in the case of regression, and  categorical variables,  as in the case of classification. It performs better for clas...