Naive Bayes algorithm: a simple multi-class classification algorithm based on the Bayes theorem. It assumes that features are independent of each other. For a given sample feature X, the probability that a sample belongs to a category H is: are data features, which are usually described by me...
The Naïve Bayes classifier is a supervised machine learning algorithm that is used for classification tasks such as text classification.
Naive Bayes, for example, assumes that features are conditionally independent given the class. KNN classification The k-nearest neighbor (KNN) algorithm is another widely used classification method. Although it can be applied to both regression and classification tasks, it is most commonly used for ...
一、Decision Tree(决策树) ——Example:for recommend app 二、Naive Bayes Algorithm(朴素贝叶斯) ——Example:for detecting Spam e-mails(垃圾邮件) 三、Gradient descent(梯度下降) ——Example:Minimize the Error 四、Linear Regression(线性回归) ——Example:Price of a house 五、(对数几率回归) Logistic ...
1.5. Naive Bayes: Naive Bayes is a probabilistic machine learning algorithm commonly used for classification tasks, especially in natural language processing and text analysis. It’s based on Bayes’ theorem and makes predictions by calculating the probability of a data point belonging to a certain...
Understanding Bayes’ theorem A strong foundation on Bayes theorem as well as Probability functions (density function and distribution function) is essential if you really wanna get an idea of intuitions behind the Naive Bayes algorithm. Bayes’ theorem is all about finding a probability (we call ...
Naive Bayes: Naive Bayesis a classification algorithm that adopts the principle of class conditional independence from Bayes’ theorem. This means that the presence of one feature does not impact the presence of another in the probability of an outcome, and each predictor has an equal effect on...
【Udacity笔记】What is Machine Learning? Teaching computers to learn to perform tasks from past experiences(recorded data) 一、Decision Tree(决策树) ——Example:for recommend app 二、Naive Bayes Algorithm(朴素贝叶斯) ——Example:for ...台大李宏毅Machine Learning 2017Fall学习笔记 (10)Tips for Deep...
In short, the algorithm is the method of learning, and the model is what results form the learning phase. The model is the conceptual model (trees, svm, linear) trained by the algorithm on your training dataset. Alexis Perrier 作家的话 ...
The choice of kernel function for an SVM algorithm is a tradeoff between accuracy and complexity. The more powerful kernel functions, such as the RBF kernel, can achieve higher accuracy than the simpler kernel functions, but they also require more data and computation time to train the SVM ...