tl;dr not with scikit-learn, but you can compute this manually with some linear algebra. i do this for your example below. also here’s a jupyter notebook with this code: https://gist.github.com/grisaitis/cf481034bb413a14d3ea851dab201d31 what and why the standard errors of your estimates are just the square root of the variances of your estimates. what’s the variance of your … Read more
It sounds like you are not feeding the same matrix of regressors X to both procedures (but see below). Here’s an example to show you which options you need to use for sklearn and statsmodels to produce identical results. import numpy as np import statsmodels.api as sm from sklearn.linear_model import LinearRegression # Generate artificial data … Read more
What I found to work was: X = your independent variables coefficients = pd.concat([pd.DataFrame(X.columns),pd.DataFrame(np.transpose(logistic.coef_))], axis = 1) The assumption you stated: that the order of regression.coef_ is the same as in the TRAIN set holds true in my experiences. (works with the underlying data and also checks out with correlations between X and y)
For generating polynomial features, I assume you are using sklearn.preprocessing.PolynomialFeatures There’s an argument in the method for considering only the interactions. So, you can write something like: poly = PolynomialFeatures(interaction_only=True,include_bias = False) poly.fit_transform(X) Now only your interaction terms are considered and higher degrees are omitted. Your new feature space becomes [x1,x2,x3,x1*x2,x1*x3,x2*x3] You can fit your … Read more
To the best of my knowledge, there is none. Writing one is pretty straightforward, though. The following gives you the constant alpha and slope beta for y = Alpha + Beta * x + epsilon: — test data (GroupIDs 1, 2 normal regressions, 3, 4 = no variance) WITH some_table(GroupID, x, y) AS ( SELECT … Read more
Linear regression doesn’t work on date data. Therefore we need to convert it into numerical value.The following code will convert the date into numerical value: import datetime as dt data_df[‘Date’] = pd.to_datetime(data_df[‘Date’]) data_df[‘Date’]=data_df[‘Date’].map(dt.datetime.toordinal)
I created an ols module designed to mimic pandas’ deprecated MovingOLS; it is here. It has three core classes: OLS : static (single-window) ordinary least-squares regression. The output are NumPy arrays RollingOLS : rolling (multi-window) ordinary least-squares regression. The output are higher-dimension NumPy arrays. PandasRollingOLS : wraps the results of RollingOLS in pandas Series & … Read more
You need to give both the fit and predict methods 2D arrays. Your x_train and x_test are currently only 1 dimensional. What is suggested by the console should work: x_train= x_train.reshape(-1, 1) x_test = x_test.reshape(-1, 1) This uses numpy’s reshape to transform your array. For example, x = [1, 2, 3] wopuld be transformed to … Read more