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Showing posts from July, 2024

ADABOOST

AdaBoost This blog post will provide you with a comprehensive overview of Adaboost, exploring the theory behind this probabilistic algorithm and demonstrating its implementation using Python libraries. Dive in to uncover the advantages and disadvantages of neural network, as well as its real-world applications across various domains. With that, enjoy your journey in QDO! What is  Adaboost AdaBoost (Adaptive Boosting) is an ensemble learning technique that combines multiple weak classifiers (often decision trees) to create a strong classifier. It works by training the weak classifiers sequentially, giving more weight to misclassified instances at each step so that subsequent classifiers focus more on the harder cases. The final prediction is made by combining the weighted votes of all weak classifiers. AdaBoost is effective at reducing bias and variance, and it’s particularly good for binary classification problems. However, it can be sensitive to noisy data and outliers. Concepts o...
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PRINCIPAL COMPONENT ANALYSIS (PCA)

PRINCIPAL COMPONENT ANALYSIS (PCA) Figure 1: PCA This blogpost will bring to you the concept of principal component analysis which is one of the commonly used descriptive analysis that emphasizes of dimensionality reduction. You will learn how to implement this machine learning model in python, its advantages and disadvantages as well as how companies benefits from this machine learning model. What is PCA PCA is a statistical dimensionality-reducing technique. It takes a large set of variables and transforms them into a smaller set, retaining most of the information in the large set. This can be done by identifying the directions along which the data varies the most. These components are orthogonal to one another, capture the maximum possible variance within the data, and hence form a powerful tool for the simplification of datasets without loss of essential patterns and relationships. Concept of  PCA One of the key concepts behind PCA concerns diminishing the complexity of high-di...
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SUPPORT VECTOR MACHINE (SVM)

SUPPORT VECTOR MACHINE(SVM) Figure 1: Support Vector Machine What is SVM An SVM is an instance of a supervised machine learning algorithm used in classification and regression. Using the created method, get the best hyperplane that will successfully separate classes. In two-dimensional space, this is just a line going through a plane and halving it into parts, each representing one class. Those points which are closest to the hyperplane and influence its position are called support vectors. The aim of the SVM is to maximize the margin between these support vectors and the hyper plane, ensuring that the model will have the best generalization capabilities. Concept of  SVM SVMs are applied for both linear and nonlinear classification with the help of the so-called kernel trick. Kernel transforms the input data into a higher-dimensional space where it is possible for such a dataset not to be linearly separable in its original space. The common kernel functions are the linear, polynomi...
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