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What makes handling Categorical Variables important?Categorical variables - like colors, cities, or education levels - cannot be directly used in machine learning models since these models only understand numbers.Categorical variable encoding solves this problem by converting these text-based categories into numerical formats that machines can process.The challenge lies in choosing the right enc..

Tree-based Ensemble Models?LightGBM, XGBoost, CatBoost, Random Forest are all tree-based ensemble models.These algorithms are popular on Kaggle because of their high prediction performance, fast learning speed, and ability to handle various data types.They show particularly powerful performance in analyzing structured data."Tree-based": These algorithms all use decision trees as their basic buil..

Weight Initialization?When training a neural network, one of the things we should care about is where we should start on the loss function to reach the optimal solution more easily and quickly.When training a neural network, the method of determining the starting position on the loss function is called Model Initialization.In particular, weights account for the largest portion of the model's par..

In Neural Networks, the activation function is a key element that enables the network to learn complex patterns and non-linearity.Reasons why activation functions are necessary:Introduction of Non-linearityWithout activation functions, neural networks would be merely combinations of linear transformations.No matter how many layers are stacked, they could be expressed as a single linear function,..

Birth of Logistic Loss FunctionIn linear regression, the goal was to find weights and bias that minimize the squared error between the actual and predicted values. Besides, the goal of classfication problems like logistic regression is to increase the actual ratio of correctly classified samples data itself. However, the ratio of correctly classified samples is not a differentiable function. In ..