Introduction: Decision tree disadvantages are powerful and widely used machine learning algorithms that excel in solving classification and regression problems. They offer interpretability, simplicity, and the ability to handle both categorical and numerical data. However, like any algorithm, decision trees come with their own set of limitations and disadvantages. In this comprehensive blog, we will delve into the drawbacks of decision trees, including overfitting, lack of robustness, sensitivity to data variations, and interpretability challenges. Overfitting: One of the main concerns with decision trees is their tendency to overfit the training data. Decision trees have the potential to create complex, deep trees that perfectly fit the training samples but perform poorly on unseen data. This occurs when the tree becomes too sensitive to the training data noise and captures insignificant patterns or outliers, resulting in reduced generalization capability. Lack of Robustnes...
Preface CRISP-DM (Cross-Industry Standard Process for Data Mining) is an extensively espoused frame that provides a structured approach to data mining systems . In this blog post, we will explore the crucial factors of CRISP-DM and its benefits for associations . Understanding CRISP - DM CRISP-DM consists of six major phases: 1. Business Understanding relating design objects and aligning them with business pretensions . 2.Data Understanding Exploring and assessing available data sources for the design . 3.Data Preparation Cleaning, transubstantiating , and preparing data for analysis . 4.Modeling picking and applying applicable data mining ways to develop models . 5.Evaluation ...
Optimizers In Deep learning is a branch of machine learning that is used to carry out difficult tasks like text categorization and speech recognition, among others. An activation function, input, output, hidden layer, loss function, and other components make up a deep learning model. Any deep learning model makes predictions based on previously unseen data and attempts to generalise the data using an algorithm. We require both an optimization method as well as an algorithm that translates examples of inputs to examples of outputs. When mapping inputs to outputs, an optimization method determines the value of the parameters (weights) that minimises the error. The effectiveness of the deep learning model is significantly impacted by these optimization methods or optimizers. They also have an impact on the model's speed training. We must adjust the weights for each epoch during deep learning model training and reduce the loss function. An optimizer is a procedure or method that alter...
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