This is post will share with you the Naive Bayes. What is Naive Bayes? 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 ...
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 ...
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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...
【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 ...
Classification is the algorithm that deals with the process of concluding the data. The classification algorithm provides limited answers like one or two and it helps the algorithm to learn from the information. Naive Bayes Decision Tree Random Forest ...
In unsupervised machine learning, the algorithm is left on its own to find structure in its input. No labels are given to the algorithm. This can be a goal in itself — discovering hidden patterns in data — or a means to an end. This is also known as “feature learning.” ...
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...
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 algo...