I'm trying to run a multinomial LogisticRegression in sklearn with a clustered dataset (that is, there are more than 1 observations for each individual, where only some features change and others remain constant per individual).
I am aware in statsmodels it is possible to account for this the following way:
mnl = MNLogit(x,y).fit(cov_type="cluster", cov_kwds={"groups": cluster_groups)
Is there a way to replicate this with the sklearn package instead?
In order to run multinomial Logistic Regression in sklearn, you can use the LogisticRegression module and then set the parameter multi_class to multinomial.
Reference: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
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I want to know what is the meaning of .linear_model in the following code -
from sklearn.linear_model import LogisticRegression
My understanding is sklearn is the library/module (both have same meaning) and LogisticRegression is the class inside this module.
But I'm not able to understand what .linear_model means?
linear_model is a module. sklearn is a package. A package is basically a module that contains other modules.
linear_model is a class of the sklearn module if contain different functions for performing machine learning with linear models.
The term linear model implies that the model is specified as a linear combination of features. Based on training data, the learning process computes one weight for each feature to form a model that can predict or estimate the target value.
It includes :
Linear regression and classification, Ridge regression and classification, Lasso, Multi-task Lasso
etc..
Check the sklearn doc for further details.
I am trying to fit a linear model without intercept (forcing the intercept to 0) using sklearn's RANSAC: RANdom SAmple Consensus algorithm. In LinearRegression one can easily set fit_intercept=False. However, this option does not seem to exist in RANSAC's list of possible parameters. Is this functionality not implemented? How should one do it? What are alternatives to sklearn's RANSAC to objectively select inliers and outliers, that allow setting the intercept to 0?
The implementation should look like this, but it raises an error:
from sklearn.linear_model import RANSACRegressor
ransac_regressor = RANSACRegressor(fit_intercept=False)
RANSAC is a wrapper around other linear regressors to implement them using random sampling consesus, thus you can simply set the base_estimator to fit_intercept=False:
from sklearn.linear_model import RANSACRegressor, LinearRegression
ransac_lm = RANSACRegressor(base_estimator=LinearRegression(fit_intercept=False))
How do I add a Regression table ( table that includes t-statistic, p-value, r^2 etc.). I've attached an image of some of my code.
sklearn is aimed at predictive modeling, so you don’t get the regression table that you are used to. An alternative in Python to sklearn is statsmodels.
See also How to get a regression summary in Python scikit like R does?
I have read about two ways statsmodel and classification_report
STATS MODEL
import statsmodels.api as sm
X = sm.add_constant(X.ravel())
results = sm.OLS(y,x).fit()
results.summary()
CLASSIFICATION REPORT
from sklearn.metrics import classifiation_report
y_preds = reg.predict(X_test)
print(classification_report(y_test,y_preds)
I have written a code that performs logistic regression with leave one out cross validation. I need to know the value of coefficients for logistic regression. But the attribute model. Coefficients_ work only after the model have used fit function. But as I have performed Cross validation so I have not used fit function to train the model.
Here is the code:
from sklearn.model_selection import LeaveOneOut
from sklearn.linear_model import LogisticRegression
reg=LogisticRegression()
loo=LeaveOneOut()
scores=cross_val_score(reg,train1,labels,cv=loo)
print(scores)
print(scores.mean())
coef = classifier.coef_
I want to know coefficient values for my features in train1 but as I have not used fit method, How can I get the values of these coefficients?
I am using the sklearn wrapper for xgboost. I would like to generate a plot of AUC for both my train and test samples for each iteration as shown in the plot below.
In sklearn you can use warm_start to iterate one at a time so you can easily stop to evaluate performance. Is there a way to do the same thing using the xgboost sklearn wrapper?