Machine Learning Algorithms with Python Code Contents of Algorithms  1.  ML Linear regression A statistical analysis technique known as "linear regression" is used to simulate the relationship between a dependent variable and one or more independent variables. 2.  ML Logistic regression  Logistic regression: A statistical method used to analyse a dataset in which there are one or more independent variables that determine an outcome. It is used to model the probability of a certain outcome, typically binary (yes/no). 3.  ML Decision trees Decision trees: A machine learning technique that uses a tree-like model of decisions and their possible consequences. It is used for classification and regression analysis, where the goal is to predict the value of a dependent variable based on the values of several independent variables. 4.  ML Random forests Random forests: A machine learning technique that uses multiple decision trees to improve the accuracy of predictions. It creates a f

Machine Learning  Decision Boundary Algorithms Decision Boundary Algorithms Concepts Decision boundary algorithms are used in machine learning to create a boundary that separates different classes or groups in a dataset. They are used to classify data points based on their features or attributes. Some popular decision boundary algorithms include decision trees, random forests, logistic regression, and support vector machines.   One example of a decision boundary algorithm is the logistic regression algorithm. To determine the likelihood of a binary outcome (such as "yes" or "no"), a binary classification procedure known as logistic regression is utilized(yes/no, true/false). It creates a decision boundary by fitting a logistic function to the training data.   Let's consider the example of a dataset containing information about a bank's customers, including their age and credit score, as well as whether they have defaulted on a loan. We can use logistic r

Ensemble method Algorithms  Concepts of Ensemble methods E nsemble methods - bagging, boosting, and stacking Ensemble methods are a type of machine learning technique that combines multiple individual models to improve the overall performance of the system. There are several ensemble methods, but three of the most commonly used ones are: Bagging (Bootstrap Aggregating) : Bagging is a technique that uses multiple independent models in parallel to make a prediction. Each model is trained on a subset of the training data, which is randomly sampled with replacement. The final prediction is made by averaging the predictions of all the individual models. Boosting : Boosting is a technique that uses multiple models sequentially. Each model is trained on the entire training set, but the weights of the training examples are adjusted based on the performance of the previous model. The final prediction is made by combining the predictions of all the individual models. Stacking : Stacking i