The Naïve Bayes classifier is a supervised machine learning algorithm that is used for classification tasks such as text classification.
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 ...
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...
Naive Bayes:This structure is based on Bayes’ theorem. A Bayesian network relies on the assumption that one feature is unrelated to other features of the same class. Support vector machines(SVM): These algorithms plot each bit of data on a graph. The algorithm then classifies points by findi...
【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 ...
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 ...
Backpropagationis a common algorithm used to train neural networks by adjusting the weights between nodes in the network based on the error between the predicted output and the actual output. Feedforwardneural networks consist of layers of nodes that process information from previous layers, with eac...
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 ...
Q13: Can you think what happens to the training as the value of k in kNN is changed? What happens when k=1? What about when k is very large? Q14: How does the accuracy change as you vary k? Q15: Do you understand how the naïve Bayes algorithm works? Q16: What observations ca...
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...