linear-regression

In the LinearRegression method in sklearn, what exactly is the fit_intercept parameter doing? [closed]

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fit_intercept=False sets the y-intercept to 0. If fit_intercept=True, the y-intercept will be determined by the line of best fit. from sklearn.linear_model import LinearRegression from sklearn.datasets import make_regression import numpy as np import matplotlib.pyplot as plt bias = 100 X = np.arange(1000).reshape(-1,1) y_true = np.ravel(X.dot(0.3) + bias) noise = np.random.normal(0, 60, 1000) y = y_true + … Read more

How to force zero interception in linear regression?

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As @AbhranilDas mentioned, just use a linear method. There’s no need for a non-linear solver like scipy.optimize.lstsq. Typically, you’d use numpy.polyfit to fit a line to your data, but in this case you’ll need to do use numpy.linalg.lstsq directly, as you want to set the intercept to zero. As a quick example: import numpy as … Read more

Linear Regression in Javascript [closed]

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What kind of linear regression? For something simple like least squares, I’d just program it myself: http://mathworld.wolfram.com/LeastSquaresFitting.html The math is not too hard to follow there, give it a shot for an hour or so and let me know if it’s too hard, I can try it. EDIT: Found someone that did it: http://dracoblue.net/dev/linear-least-squares-in-javascript/159/

Linear Regression :: Normalization (Vs) Standardization

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Note that the results might not necessarily be so different. You might simply need different hyperparameters for the two options to give similar results. The ideal thing is to test what works best for your problem. If you can’t afford this for some reason, most algorithms will probably benefit from standardization more so than from … Read more

How to extract the regression coefficient from statsmodels.api?

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You can use the params property of a fitted model to get the coefficients. For example, the following code: import statsmodels.api as sm import numpy as np np.random.seed(1) X = sm.add_constant(np.arange(100)) y = np.dot(X, [1,2]) + np.random.normal(size=100) result = sm.OLS(y, X).fit() print(result.params) will print you a numpy array [ 0.89516052 2.00334187] – estimates of intercept … Read more