Learn about classification in machine learning, looking at what it is, how it's used, and some examples of classification algorithms.
10.什么是tree stump?什么是decision stump learning? tree stump就是木桩的意思,在决策树里,指的是一个结点。 decision stump learning就是决定如何选取一个结点的feature。 11.如何选择feature? 选择能使错误率到最低的feature。 12.决策树是统计学习方法吗? 是的,虽然好像只是比较简单的统计(计算错误率)。 13....
Which statement best describes the task of “classification” in machine learning?哪一个是机器学习中“分类”任务的正确描述?A.To assign a category to each item. 为每个项目分配一个类别。B.To find the distribution of inputs in some space. 发现某个空间中输入的分布。C.To group data objects. 对...
《Machine Learning:Classification》课程第1章Linear Classifier & Logistic Classifier问题集 1.regression的outcome是连续值,classification的outcome是离散值,可以认为classification是一种特殊的regression嘛? 不能这样简单认为,一个区别是regression的outcome是有大小关系的,而classification的outcome是没有大小关系的,比如三个...
Linear regression assumes that the reponse variable is quantitative variable. But in many situations, the response could be qualitative variable, such as the status of marriage, gender, and so on. Usually qualitative variables are referred to as categorical variables.Using statistical learning methods ...
Create and understand classification models in machine learning: Episode 06 Classification means assigning items into categories or can also be thought of automated decision making. Here we introduce classification models through logistic regression, providing you with a stepping-stone toward more comp...
Supervised and unsupervised machine learning methods make a classification decision based on feature inputs.
For example, a microcomputer may be supplied with a sensor dataset of temperature, light, and humidity. Then, it may be modeled to predict day or night or estimate the time of the day. In such a case, in contrast to a typical embedded program routine, a machine learning model has better...
I suggest to always try agradient boostingalgorithm (like XGBoost). It’s a machine learning technique that produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. Basically it’s similar to a Random Forest with the difference that every tree...
The question depends on one core conception that is omnipresent in the studying and career of machine learning,variance and bias tradeoff. If K classes share the common covariance matrix, the LDA has a linear decision boundary, which means that the coefficients of LDA model should be linear. I...