基本机器学习模型的流程图

根据可用的反馈，机器学习任务分为三类：

1. 监督学习：这些是基于输入和输出的人工构建模型。
2. 无监督学习：这些模型依赖于人工输入。没有给学习算法贴上标签，模型必须自己找出结构。
3. 强化学习：这些是人工输入的模型。没有给学习算法贴上标签。该算法通过给定的奖励和惩罚进行学习。

可用于每个类别的算法是：

Algorithm	Supervised	Unsupervised	Reinforcement
Linear	1	0	0
Logistic	1	0	0
K-Means	1	1	0
Anomaly Detection	1	1	0
Neural Net	1	1	1
KNN	1	0	0
Decision Tee	1	0	0
Random Forest	1	0	0
SVM	1	0	0
Naive Bayes	1	0	0

下表给出了机器学习功能和各种任务的用途。要了解有关算法的更多信息，请单击此处。

Category	Algorithm	Function	Use
Basic Regression	Linear	linear_model.LinearRegression()	Lots of numerical data
	Logistic	linear_model.LogisticRegression()	Target variable is categorical
Cluster Analysis	K-Means	cluster.KMeans()	Similar datum into groups based on centroids
	Anomaly Detection	covariance.EllipticalEnvelope()	Finding outliers through grouping
Classification	Neural Net	neural_network.MLPClassifier()	Complex relationships. Prone to over fitting.
	K-NN	neighbors.KNeighborsClassifier()	Group membership based on proximity
	Decision Tee	tree.DecisionTreeClassifier()	If/then/else. Non-contiguous data. Can also be regression.
	Random Forest	ensemble.RandomForestClassifier()	Find best split randomly. Can also be regression
	SVM	svm.SVC()  svm.LinearSVC()	Maximum margin classifier. Fundamental. Data Science algorithm
	Naive Bayes	GaussianNB() MultinominalNB() BernoulliNB()	Updating knowledge step by step with new info
Feature Reduction	T-DISTRIB Stochastic NEIB Embedding	manifold.TSNE()	Visual high dimensional data. Convert similarity to joint probabilities
	Principle Component Analysis	decomposition.PCA()	Distill feature space into components that describe the greatest variance
	Canonical Correlation Analysis	decomposition.CCA()	Making sense of cross-correlation matrices
	Linear Discriminant Analysis	lda.LDA()	Linear combination of features that separates classes

The flowchart given below will help you give a rough guide of each estimator that will help to know more about the task and the ways to solve it using various ML techniques.