LinearRegression与svm.SVR之间的差异(内核=“线性") [英] difference between LinearRegression and svm.SVR(kernel="linear")

问题描述

首先，此论坛上存在与该论坛非常相似的问题，但请相信我没有匹配的内容，因此请不要重复.

First there are questions on this forum very similar to this one but trust me none matches so no duplicating please.

我遇到了使用scikit的sklearn进行线性回归的两种方法，但我无法理解两者之间的区别，尤其是在第一个代码中调用了train_test_split()方法的情况下，而在另一种直接适合的方法中调用了方法.

I have encountered two methods of linear regression using scikit's sklearn and I am failing to understand the difference between the two, especially where in first code there's a method train_test_split() called while in the other one directly fit method is called.

我正在研究多种资源，而这个问题对我来说很令人困惑.

I am studying with multiple resources and this single issue is very confusing to me.

首先使用SVR

X = np.array(df.drop(['label'], 1))

X = preprocessing.scale(X)

y = np.array(df['label'])

X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.2)

clf = svm.SVR(kernel='linear')

clf.fit(X_train, y_train)

confidence = clf.score(X_test, y_test)

第二个是这个

# Split the data into training/testing sets
diabetes_X_train = diabetes_X[:-20]
diabetes_X_test = diabetes_X[-20:]

# Split the targets into training/testing sets
diabetes_y_train = diabetes.target[:-20]
diabetes_y_test = diabetes.target[-20:]

# Create linear regression object
regr = linear_model.LinearRegression()

# Train the model using the training sets
regr.fit(diabetes_X_train, diabetes_y_train)

# Make predictions using the testing set
diabetes_y_pred = regr.predict(diabetes_X_test)

所以我的主要重点是使用svr(kernel ="linear")和使用LinearRegression()之间的区别

So my main focus is the difference between using svr(kernel="linear") and using LinearRegression()

推荐答案

这是我发现的:

直观地讲，作为所有回归变量，它都试图通过最小化成本函数使一条线适合数据.但是，关于SVR的有趣部分是您可以部署非线性内核.在这种情况下，您将结束非线性回归，即拟合曲线而不是直线. 此过程基于内核技巧以及对偶而非原始中解决方案/模型的表示.也就是说，模型表示为训练点的组合，而不是特征和某些权重的函数.同时，基本算法保持不变:非线性过程中唯一真正的变化是核函数，其从简单的内积变为某些非线性函数.

Intuitively, as all regressors it tries to fit a line to data by minimising a cost function. However, the interesting part about SVR is that you can deploy a non-linear kernel. In this case you end making non-linear regression, i.e. fitting a curve rather than a line. This process is based on the kernel trick and the representation of the solution/model in the dual rather than in the primal. That is, the model is represented as combinations of the training points rather than a function of the features and some weights. At the same time the basic algorithm remains the same: the only real change in the process of going non-linear is the kernel function, which changes from a simple inner product to some non linear function.

因此SVR也允许非线性拟合问题，而LinearRegression()仅用于具有直线的简单线性回归(在两种情况下都可以包含任意数量的特征).

So SVR allows non linear fitting problems as well while LinearRegression() is only for simple linear regression with straight line (may contain any number of features in both cases).

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