-
AdaBoost Algorithm
AdaBoost Algorithm Table Of Contents: Introduction What Is the AdaBoost Algorithm? Understanding the Working of the AdaBoost Algorithm (1) Introduction Boosting is a machine learning ensemble technique that combines multiple weak learners to create a strong learner. The term “boosting” refers to the idea of boosting the performance of weak models by iteratively training them on different subsets of the data. The main steps involved in a boosting algorithm are as follows: Initialize the ensemble: Initially, each instance in the training data is given equal weight, and a weak learner (e.g., a decision tree) is trained on the data. Iteratively
-
Boosting Algorithms
Boosting Algorithms Table Of Contents: What Is Boosting In Machine Learning? Types Of Boosting Algorithm. (1) What Is Boosting? Boosting is a machine learning ensemble technique that combines multiple weak learners (typically decision trees) to create a strong learner. The main idea behind boosting algorithms is to iteratively train weak models in a sequential manner, where each subsequent model focuses on correcting the mistakes made by previous models. This iterative process gradually improves the overall predictive performance of the ensemble. (2) Types Of Boosting Algorithms. AdaBoost (Adaptive Boosting): AdaBoost assigns weights to each training instance and adjusts them based on
-
Bias Vs Variance !!
Bias Vs Variance Table Of Contents: Introduction. Errors In Machine Learning. What Is Bias? Why Does Bias Occurs In Model? Effect Of Bias In Our Model. Way To Reduce High Bias. What Is Variance? Why Does Variance Occurs In Model? Effect Of Variance In Our Model. Way To Reduce High Variance. What Is Bias Variance Trade-Off? (1) Introduction. Bias and variance are two important concepts in machine learning that help in understanding the behaviour and performance of a model. They represent different sources of error in a machine learning algorithm and can provide insights into how well the model is
-
Regularization In Machine Learning.
Regularization In Machine Learning Table Of Contents: What Is Regularization? Types Of Regularization Techniques. L1 Regularization (Lasso Regularization). L2 Regularization (Ridge Regularization). Elastic Net Regularization. (1) What Is Regularization? Regularization in machine learning is a technique used to prevent overfitting and improve the generalization performance of a model. Overfitting occurs when a model learns to fit the training data too closely, capturing noise or irrelevant patterns that do not generalize well to new, unseen data. Regularization introduces additional constraints or penalties to the learning process to prevent the model from becoming too complex or sensitive to the training data. (2)
-
K – Means Clustering Algorithm.
K – Means Clustering Table Of Contents: What Is Clustering? What Is The K-Means Algorithm? How Does The K-Means Algorithm Work? How to Choose the Right Number of Clusters in K-Means Clustering? Advantages & Disadvantages Of K-Means Clustering. Applications Of K-Means Clustering. (1) What Is Clustering ? Clustering is a technique used in machine learning and data analysis to group similar objects or data points together based on their inherent characteristics or patterns. It is an unsupervised learning method, meaning that it does not rely on labelled data or predefined categories. The goal of clustering is to identify natural groupings
-
Naive Bayes Algorithm Paper Work
-
Naive Bayes Algorithm
Naive Bayes Algorithm Table Of Contents: What Is Naive Bayes Algorithm? What Is Conditional Probability? What Is Bayes Theorem? Why Is It Called Naive Bayes? Assumptions Of Naive Bayes Algorithm. What Is Bayesian Probability? How Does The Naive Bayes Algorithm Works? Types Of Naive Bayes Model. Pros & Cons Of Naive Bayes Algorithm. Applications Of Naive Bayes Algorithm. (1) What Is Naive Bayes Algorithm? The Naive Bayes algorithm is a probabilistic machine learning algorithm commonly used for classification tasks. It is based on Bayes’ theorem, which describes the probability of an event given prior knowledge or evidence. The “naive” assumption
-
K – Nearest Neighbors Algorithm
K – Nearest Neighbors Table Of Contents: What Is K – Nearest Neighbor Algorithm? How Does The KNN Algorithm Work? How Do We Choose The Factore K? Distance Metrics Used In KNN Algorithm. Advantages & Disadvantages Of KNN Algorithm. Applications Of KNN Algorithm. (1) What Is K – Nearest Neighbor? The k-nearest neighbours (k-NN) algorithm is a popular supervised machine learning algorithm used for both classification and regression tasks. It operates based on the principle that data points with similar features tend to belong to the same class or have similar output values. It is widely disposable in real-life scenarios
-
Support Vector Machine
Support Vector Machine Table Of Contents: What Is a Support Vector Machine? How Does Support Vector Machine Work? Types Of Support Vector Machine Algorithms. Mathematical Intuition Behind Support Vector Machine. Margin In Support Vector Machine. Optimization Function and Its Constraints. Soft Margin SVM. Kernels In Support Vector Machine. How To Choose A Right Kernel? (1) What Is Support Vector Machine? The Support Vector Machine (SVM) algorithm is a supervised machine learning algorithm used for classification and regression tasks. It is particularly effective in solving binary classification problems but can also be extended to multi-class classification. SVMs can be used for
-
Weak Learner vs. Strong Learner.
Weak Learner Vs. Strong Learner Table Of Contents: Introduction. Weak Learner. Strong Learner. Conclusion. (1) Introduction: In machine learning, the terms “strong learner” and “weak learner” refer to the performance and complexity of predictive models within an ensemble or learning algorithm. These terms are often used in the context of boosting algorithms. (2) Weak Learner: A weak learner is a model that performs slightly better than random guessing or has limited predictive power on its own. Weak learners are typically simple and have low complexity, such as decision stumps (a decision tree with only one split), shallow decision trees, or
