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Python regressors library summary function returns ValueError for Logistic regression
I'm using python inbulit boston dataset from sklearn with CHAS as my target variable.
I built Logistic Regression model from sklearn pkg.I'm using regressors library to get the summary statistics of the model output but i'm facing the following error. pleasee help me on this and kindly let me know if you need further information
find more about regressors library in below link: [1]:
https://regressors.readthedocs.io/en/latest/usage.html
Please find the below python code which i used for model building:
import numpy as np
from sklearn import datasets
import pandas as pd
bostonn = datasets.load_boston()
boston = pd.DataFrame(bostonn.data , columns= bostonn['feature_names'])
print(boston.head())
X = boston.drop('CHAS' , axis =1)
y = boston.CHAS.astype('category')
from sklearn.linear_model import LogisticRegression
from regressors import stats
log_mod=LogisticRegression(random_state=123)
model=log_mod.fit(X,y)
stats.summary(model, X, y , xlabels=None)
I'm getting the following error:
ValueErrorTraceback (most recent call last)
in ()
1 #xlabels = boston.feature_names[which_betas]
----> 2 stats.summary(model, X, y ,xlabels=None)
251 )
252 coef_df['Estimate'] = np.concatenate(
--> 253 (np.round(np.array([clf.intercept_]), 6), np.round((clf.coef_), 6)))
254 coef_df['Std. Error'] = np.round(coef_se(clf, X, y), 6)
255 coef_df['t value'] = np.round(coef_tval(clf, X, y), 4)
ValueError: all the input array dimensions except for the concatenation axis must match exactly
ValueError: all the input array dimensions except for the concatenation axis must match exactly
There are other posts which has the similar error but those solution didn't help
my problem.The attached above link has the information about how the summary function actually works.kindly let me know if you need further information.
python scikit-learn logistic-regression statsmodels valueerror
edited Jan 4 at 7:57
Vivek Kumar
16.8k42156
|
show 2 more comments
I'm using python inbulit boston dataset from sklearn with CHAS as my target variable.
I built Logistic Regression model from sklearn pkg.I'm using regressors library to get the summary statistics of the model output but i'm facing the following error. pleasee help me on this and kindly let me know if you need further information
find more about regressors library in below link: [1]:
https://regressors.readthedocs.io/en/latest/usage.html
Please find the below python code which i used for model building:
import numpy as np
from sklearn import datasets
import pandas as pd
bostonn = datasets.load_boston()
boston = pd.DataFrame(bostonn.data , columns= bostonn['feature_names'])
print(boston.head())
X = boston.drop('CHAS' , axis =1)
y = boston.CHAS.astype('category')
from sklearn.linear_model import LogisticRegression
from regressors import stats
log_mod=LogisticRegression(random_state=123)
model=log_mod.fit(X,y)
stats.summary(model, X, y , xlabels=None)
I'm getting the following error:
ValueErrorTraceback (most recent call last)
in ()
1 #xlabels = boston.feature_names[which_betas]
----> 2 stats.summary(model, X, y ,xlabels=None)
251 )
252 coef_df['Estimate'] = np.concatenate(
--> 253 (np.round(np.array([clf.intercept_]), 6), np.round((clf.coef_), 6)))
254 coef_df['Std. Error'] = np.round(coef_se(clf, X, y), 6)
255 coef_df['t value'] = np.round(coef_tval(clf, X, y), 4)
ValueError: all the input array dimensions except for the concatenation axis must match exactly
ValueError: all the input array dimensions except for the concatenation axis must match exactly
There are other posts which has the similar error but those solution didn't help
my problem.The attached above link has the information about how the summary function actually works.kindly let me know if you need further information.
python scikit-learn logistic-regression statsmodels valueerror
edited Jan 4 at 7:57
Vivek Kumar
16.8k42156
@James Z, can u help me on this
– user10857548
Jan 3 at 4:42
Sorry, don't know anything about this
– James Z
Jan 3 at 15:31
LogisticRegressionis not a regressor. Its a classifier.
– Vivek Kumar
Jan 4 at 7:57
What is the output ofprint(X.shape, y.shape)?
– BlackBear
Jan 4 at 7:59
@BlackBear It has nothing to do withX, y, but the shape ofcoef_andintercept_that are learnt when the model isfit(). That is different in regressors and classifiers in scikit-learn and also depend on other factors. The library OP is using is about the regression models where asLogisticRegression(despite its name) is a classifier.
– Vivek Kumar
Jan 4 at 8:07
|
show 2 more comments
I'm using python inbulit boston dataset from sklearn with CHAS as my target variable.
I built Logistic Regression model from sklearn pkg.I'm using regressors library to get the summary statistics of the model output but i'm facing the following error. pleasee help me on this and kindly let me know if you need further information
find more about regressors library in below link: [1]:
https://regressors.readthedocs.io/en/latest/usage.html
Please find the below python code which i used for model building:
import numpy as np
from sklearn import datasets
import pandas as pd
bostonn = datasets.load_boston()
boston = pd.DataFrame(bostonn.data , columns= bostonn['feature_names'])
print(boston.head())
X = boston.drop('CHAS' , axis =1)
y = boston.CHAS.astype('category')
from sklearn.linear_model import LogisticRegression
from regressors import stats
log_mod=LogisticRegression(random_state=123)
model=log_mod.fit(X,y)
stats.summary(model, X, y , xlabels=None)
I'm getting the following error:
ValueErrorTraceback (most recent call last)
in ()
1 #xlabels = boston.feature_names[which_betas]
----> 2 stats.summary(model, X, y ,xlabels=None)
251 )
252 coef_df['Estimate'] = np.concatenate(
--> 253 (np.round(np.array([clf.intercept_]), 6), np.round((clf.coef_), 6)))
254 coef_df['Std. Error'] = np.round(coef_se(clf, X, y), 6)
255 coef_df['t value'] = np.round(coef_tval(clf, X, y), 4)
ValueError: all the input array dimensions except for the concatenation axis must match exactly
ValueError: all the input array dimensions except for the concatenation axis must match exactly
There are other posts which has the similar error but those solution didn't help
my problem.The attached above link has the information about how the summary function actually works.kindly let me know if you need further information.
python scikit-learn logistic-regression statsmodels valueerror
edited Jan 4 at 7:57
Vivek Kumar
16.8k42156
I'm using python inbulit boston dataset from sklearn with CHAS as my target variable.
I built Logistic Regression model from sklearn pkg.I'm using regressors library to get the summary statistics of the model output but i'm facing the following error. pleasee help me on this and kindly let me know if you need further information
find more about regressors library in below link: [1]:
https://regressors.readthedocs.io/en/latest/usage.html
Please find the below python code which i used for model building:
import numpy as np
from sklearn import datasets
import pandas as pd
bostonn = datasets.load_boston()
boston = pd.DataFrame(bostonn.data , columns= bostonn['feature_names'])
print(boston.head())
X = boston.drop('CHAS' , axis =1)
y = boston.CHAS.astype('category')
from sklearn.linear_model import LogisticRegression
from regressors import stats
log_mod=LogisticRegression(random_state=123)
model=log_mod.fit(X,y)
stats.summary(model, X, y , xlabels=None)
I'm getting the following error:
ValueErrorTraceback (most recent call last)
in ()
1 #xlabels = boston.feature_names[which_betas]
----> 2 stats.summary(model, X, y ,xlabels=None)
251 )
252 coef_df['Estimate'] = np.concatenate(
--> 253 (np.round(np.array([clf.intercept_]), 6), np.round((clf.coef_), 6)))
254 coef_df['Std. Error'] = np.round(coef_se(clf, X, y), 6)
255 coef_df['t value'] = np.round(coef_tval(clf, X, y), 4)
ValueError: all the input array dimensions except for the concatenation axis must match exactly
ValueError: all the input array dimensions except for the concatenation axis must match exactly
There are other posts which has the similar error but those solution didn't help
my problem.The attached above link has the information about how the summary function actually works.kindly let me know if you need further information.
python scikit-learn logistic-regression statsmodels valueerror
python scikit-learn logistic-regression statsmodels valueerror
edited Jan 4 at 7:57
Vivek Kumar
16.8k42156
edited Jan 4 at 7:57
Vivek Kumar
16.8k42156
edited Jan 4 at 7:57
Vivek Kumar
16.8k42156
edited Jan 4 at 7:57
Vivek Kumar
16.8k42156
edited Jan 4 at 7:57
Vivek Kumar
16.8k42156
16.8k42156
asked Jan 2 at 17:56
user10857548
@James Z, can u help me on this
– user10857548
Jan 3 at 4:42
Sorry, don't know anything about this
– James Z
Jan 3 at 15:31
LogisticRegressionis not a regressor. Its a classifier.
– Vivek Kumar
Jan 4 at 7:57
What is the output ofprint(X.shape, y.shape)?
– BlackBear
Jan 4 at 7:59
@BlackBear It has nothing to do withX, y, but the shape ofcoef_andintercept_that are learnt when the model isfit(). That is different in regressors and classifiers in scikit-learn and also depend on other factors. The library OP is using is about the regression models where asLogisticRegression(despite its name) is a classifier.
– Vivek Kumar
Jan 4 at 8:07
|
show 2 more comments
@James Z, can u help me on this
– user10857548
Jan 3 at 4:42
Sorry, don't know anything about this
– James Z
Jan 3 at 15:31
LogisticRegressionis not a regressor. Its a classifier.
– Vivek Kumar
Jan 4 at 7:57
What is the output ofprint(X.shape, y.shape)?
– BlackBear
Jan 4 at 7:59
@BlackBear It has nothing to do withX, y, but the shape ofcoef_andintercept_that are learnt when the model isfit(). That is different in regressors and classifiers in scikit-learn and also depend on other factors. The library OP is using is about the regression models where asLogisticRegression(despite its name) is a classifier.
– Vivek Kumar
Jan 4 at 8:07
@James Z, can u help me on this
– user10857548
Jan 3 at 4:42
@James Z, can u help me on this
– user10857548
Jan 3 at 4:42
Sorry, don't know anything about this
– James Z
Jan 3 at 15:31
Sorry, don't know anything about this
– James Z
Jan 3 at 15:31
LogisticRegression is not a regressor. Its a classifier.– Vivek Kumar
Jan 4 at 7:57
LogisticRegression is not a regressor. Its a classifier.– Vivek Kumar
Jan 4 at 7:57
What is the output of
print(X.shape, y.shape)?– BlackBear
Jan 4 at 7:59
What is the output of
print(X.shape, y.shape)?– BlackBear
Jan 4 at 7:59
@BlackBear It has nothing to do with
X, y, but the shape of coef_ and intercept_ that are learnt when the model is fit(). That is different in regressors and classifiers in scikit-learn and also depend on other factors. The library OP is using is about the regression models where as LogisticRegression (despite its name) is a classifier.– Vivek Kumar
Jan 4 at 8:07
@BlackBear It has nothing to do with
X, y, but the shape of coef_ and intercept_ that are learnt when the model is fit(). That is different in regressors and classifiers in scikit-learn and also depend on other factors. The library OP is using is about the regression models where as LogisticRegression (despite its name) is a classifier.– Vivek Kumar
Jan 4 at 8:07
|
show 2 more comments
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@James Z, can u help me on this
– user10857548
Jan 3 at 4:42
Sorry, don't know anything about this
– James Z
Jan 3 at 15:31
LogisticRegressionis not a regressor. Its a classifier.– Vivek Kumar
Jan 4 at 7:57
What is the output of
print(X.shape, y.shape)?– BlackBear
Jan 4 at 7:59
@BlackBear It has nothing to do with
X, y, but the shape ofcoef_andintercept_that are learnt when the model isfit(). That is different in regressors and classifiers in scikit-learn and also depend on other factors. The library OP is using is about the regression models where asLogisticRegression(despite its name) is a classifier.– Vivek Kumar
Jan 4 at 8:07