Logit Model Gradient Descent for Back Propagation
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How can I implement Back Propagation with Logit Model getting an accuracy of 90% need to propagate backward for future predictions.
This is my Python Code::
import pandas as pd
import numpy as np
import statsmodels.api as sm
from sklearn.model_selection import train_test_split
fields = ["articleStatus","keywordcount","avgKeywordSum","prominaceRatio"]
df=pd.read_csv("Processing_result.csv",skipinitialspace=True,usecols=fields)
X = df[['keywordcount','prominaceRatio']]
y = df[['articleStatus']]
y = np.ravel(y)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state= 0)
X2 = sm.add_constant(X)
est = sm.Logit(y, X2)
est2 = est.fit()
print(est2.summary())
machine-learning python logistic-regression
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$begingroup$
How can I implement Back Propagation with Logit Model getting an accuracy of 90% need to propagate backward for future predictions.
This is my Python Code::
import pandas as pd
import numpy as np
import statsmodels.api as sm
from sklearn.model_selection import train_test_split
fields = ["articleStatus","keywordcount","avgKeywordSum","prominaceRatio"]
df=pd.read_csv("Processing_result.csv",skipinitialspace=True,usecols=fields)
X = df[['keywordcount','prominaceRatio']]
y = df[['articleStatus']]
y = np.ravel(y)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state= 0)
X2 = sm.add_constant(X)
est = sm.Logit(y, X2)
est2 = est.fit()
print(est2.summary())
machine-learning python logistic-regression
New contributor
$endgroup$
add a comment |
$begingroup$
How can I implement Back Propagation with Logit Model getting an accuracy of 90% need to propagate backward for future predictions.
This is my Python Code::
import pandas as pd
import numpy as np
import statsmodels.api as sm
from sklearn.model_selection import train_test_split
fields = ["articleStatus","keywordcount","avgKeywordSum","prominaceRatio"]
df=pd.read_csv("Processing_result.csv",skipinitialspace=True,usecols=fields)
X = df[['keywordcount','prominaceRatio']]
y = df[['articleStatus']]
y = np.ravel(y)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state= 0)
X2 = sm.add_constant(X)
est = sm.Logit(y, X2)
est2 = est.fit()
print(est2.summary())
machine-learning python logistic-regression
New contributor
$endgroup$
How can I implement Back Propagation with Logit Model getting an accuracy of 90% need to propagate backward for future predictions.
This is my Python Code::
import pandas as pd
import numpy as np
import statsmodels.api as sm
from sklearn.model_selection import train_test_split
fields = ["articleStatus","keywordcount","avgKeywordSum","prominaceRatio"]
df=pd.read_csv("Processing_result.csv",skipinitialspace=True,usecols=fields)
X = df[['keywordcount','prominaceRatio']]
y = df[['articleStatus']]
y = np.ravel(y)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state= 0)
X2 = sm.add_constant(X)
est = sm.Logit(y, X2)
est2 = est.fit()
print(est2.summary())
machine-learning python logistic-regression
machine-learning python logistic-regression
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asked 39 secs ago
Ankit srivastavaAnkit srivastava
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Ankit srivastava is a new contributor. Be nice, and check out our Code of Conduct.
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