###### Decission Tree

A decision tree is a type of machine learning algorithm used for classification and regression tasks. It works by recursively partitioning the input data into subsets based on the values of the input features. The algorithm begins at the root of the tree, where it evaluates a test condition based on the input feature values. Based on the outcome of this test, the algorithm moves to one of the child nodes of the root, where another test is performed, and so on, until a leaf node is reached. The lFew readers###### Logistic Regression

Algorithm learns a function that divides the data into two parts. It is used to solve classification problems. Formal definition In statistics, the logistic model (or logit model) is a statistical model that models the probability of an event taking place. In machine learning the idea is estimating the parameters of a logistic function. Intuition In statistical learning (as a part of machine lFew readers###### Extra-trees

Extra Trees (Extremely Randomized Trees) is an ensemble machine learning algorithm that is based on decision trees. It is similar to the Random Forest algorithm, but with a few key differences in the way the decision trees are constructed. Like Random Forest, Extra Trees also creates a collection of decision trees, each of which is trained on a different subset of the data. The key difference is that in Extra Trees, at each split in the decision tree, instead of selecting the best split point fFew readers###### XGBoost

XGBoost (eXtreme Gradient Boosting) is a gradient boosting algorithm that can be used for supervised learning tasks such as classification and regression. The main idea behind XGBoost is to fit an ensemble of decision trees to the training data. The ensemble of decision trees is created by iteratively adding new trees to the model, where each tree is trained to correct the mistakes of the previous trees. The final output is a combination of the predictions from all of the trees in the ensemble.Few readers###### Linear Regression

Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. It is used to find the best linear relationship between the input variables and the output variable. The basic idea behind linear regression is that there is a linear relationship between the input variables (also known as independent variables or predictors) and the output variable (also known as the dependent variable or response). The goal of linear regFew readers###### Light GBM

LightGBM is a gradient boosting framework that uses tree-based learning algorithms. It is designed to be efficient and scalable, and is particularly well suited for large datasets or datasets with many features. LightGBM is an open-source project developed by Microsoft, it is based on the popular GBM algorithm, but with several enhancements that make it faster and more accurate. A Sample Tree in LightGBM (https://storage.crisp.chat/users/helpdesk/website/ac02aa4ca9b97000/21lightgbmxp3r0.pngFew readers###### Nearest Neighbor

K-Nearest Neighbors (KNN) is a supervised machine learning algorithm used for classification and regression. The main idea behind the algorithm is to find the k-nearest data points (neighbors) in the feature space to a given data point, and use the majority class (or average value in case of regression) of these neighbors to predict the class (or value) of the given data point. The algorithm works by: Saving all the training data points and their corresponding classes (or values in case ofFew readers###### CatBoost

CatBoost is a gradient boosting algorithm developed by Yandex. Although it is specifically designed to handle categorical data, which is data that consists of categorical variables, such as gender or product categories, it can also work with numerical, and text features. CatBoost works by combining multiple decision trees to make a prediction. Each decision tree is trained on a different subset of the data, and the final prediction is made by combining the predictions of each individual tree. TFew readers###### Random Forest

Random Forest is an ensemble machine learning algorithm that is based on decision trees. The basic idea behind random forest is to combine multiple decision trees in order to reduce overfitting and improve the overall performance of the model. The algorithm works by training a large number of decision trees on different subsets of the data, and then averaging the predictions of all the trees to make a final prediction. The subsets of data used to train each tree are selected randomly, with repFew readers###### Neural Networks

A neural network is a type of machine learning algorithm that is modeled after the structure and function of the human brain. It consists of layers of interconnected "neurons," which process and transmit information. The basic building block of a neural network is the neuron, which takes in inputs, performs a computation on them, and produces an output. The inputs are typically real-valued numbers, and the computation performed by each neuron is a simple mathematical operation such as a dot prFew readers