Learn about classification in machine learning, looking at what it is, how it's used, and some examples of classification algorithms.
Machine learning is a field of study and is concerned with algorithms that learn from examples. Classification is a task that requires the use of machine learning algorithms that learn how to assign a class label to examples from the problem domain. An easy to understand example is classifying ...
监督学习(Supervised Learning) 现实世界中应用最为广泛,涵盖于本课程第一、第二部分 非监督学习(Unsupervised Learning) 涵盖于本课程第三部分 强化学习(Reinforcement Learning) 本课程暂不多作介绍。 2. 监督学习 监督学习的关键特征是给予学习算法一些示例去学习,包括正确的和错误的示例。 2.1 回归(Regression) 根据...
Cross-Validation Overfitting is a common problem in machine learning and it occurs in most models. K-fold cross-validation can be conducted to verify that the model is not overfitted. In this method, the data set is randomly partitioned into k-mutually exclusive subsets, each approximately equa...
I believe visualization is the best tool for data analysis, but you need to know what kind of plots are more suitable for the different types of variables. Therefore, I’ll provide the code to plot the appropriate visualization for different examples. ...
【解释】The model has high variance (it overfits the training data). Adding data (more training examples) can help. 第3 个问题:Suppose you have a regularized linear regression model. If you increase the regularization parameter λ, what do you expect to happen to the parameters w1,w2,......
Data Classification Examples Here are some examples of data classification: Personal Identifiable Information (PII): This classification includes data that can identify an individual, such as names, addresses, social security numbers, or phone numbers. It is classified as sensitive and requires strict ...
function [J, grad] = lrCostFunction(theta, X, y, lambda) % Initialize some useful values m = length(y); % number of training examples J = 0; grad = zeros(size(theta)); n1 = length(theta); % X size: m * (n+1), theta size: (n+1) * 1, h size: m * 1 ...
Classification is a type of supervised machine learning in which an algorithm “learns” to classify new observations from examples of labeled data. To explore classification models interactively, use the Classification Learner app. For greater flexibility, you can pass predictor or feature data with ...
These diagonal choices are specific examples of a naive Bayes classifier, because they assume the variables are conditionally independent given the class label. Naive Bayes classifiers are among the most popular classifiers. While the assumption of class-conditional independence between variables is not ...