statsmodels

Say I fit a model in statsmodels mod = smf.ols('dependent ~ first_category + second_category + other', data=df).fit() When I do mod.summary() I may see the following: Warnings: [1] The condition number is large, 1.59e+05. This might indicate that there are strong multicollinearity or other numerical problems. Sometimes the warning is different (e.g. based on eigenvalues of the design matrix). How can I capture high-multi-collinearity conditions in a variable? Is this warning stored somewhere in the model object? Also, where can I find a description of the fields in summary() ? You can detect

I'd like to choose the best algorithm for future. I found some solutions, but I didn't understand which R-Squared value is correct. For this, I divided my data into two as test and training, and I printed two different R squared values ​​below. import statsmodels.api as sm from sklearn.linear_model import LinearRegression from sklearn.metrics import r2_score lineer = LinearRegression() lineer.fit(x_train,y_train) lineerPredict = lineer.predict(x_test) scoreLineer = r2_score(y_test, lineerPredict) # First R-Squared model = sm.OLS(lineerPredict, y_test) print(model.fit().summary()) # Second R

Is there an existing function to estimate fixed effect (one-way or two-way) from Pandas or Statsmodels. There used to be a function in Statsmodels but it seems discontinued. And in Pandas, there is something called plm , but I can't import it or run it using pd.plm() . As noted in the comments, PanelOLS has been removed from Pandas as of version 0.20.0. So you really have three options: If you use Python 3 you can use linearmodels as specified in the more recent answer: https://stackoverflow.com/a/44836199/3435183 Just specify various dummies in your statsmodels specification, e.g. using pd

I have a pandas data frame and I would like to able to predict the values of column A from the values in columns B and C. Here is a toy example: import pandas as pd df = pd.DataFrame({"A": [10,20,30,40,50], "B": [20, 30, 10, 40, 50], "C": [32, 234, 23, 23, 42523]}) Ideally, I would have something like ols(A ~ B + C, data = df) but when I look at the examples from algorithm libraries like scikit-learn it appears to feed the data to the model with a list of rows instead of columns. This would require me to reformat the data into lists inside lists, which seems to defeat the purpose of using