Sklearn - My Cheat Sheet
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Sometimes I get just really lost with all available commands and tricks one can make on sklearn. This way, I really wanted a place to gather my tricks that I really don’t want to forget.
How to check results of accuracy for each of the classes available on a classification problem?
from sklearn.metrics import classification_report
classification_report(y_test, y_pred, target_names=['A', 'B', 'C']
# Results:
precision recall f1-score
A 0.9 ... ...
B 0.9 ... ...
C 0.9 ... ...
avg/total 0.9 ... ...
How to normalize features?
from sklearn import preprocessing
normalized_X = preprocessing.normalize(X)
How to create a bag of words dataframe matrix
import pandas as pd
from sklearn.feature_extraction.text imprt CountVectorizer
documents = ['Hello, how are you!',
'Win money, win from home.',
'Call me now.',
'Hello, Call hello you tomorrow?']
count_vector = CountVectorizer()
count_vector.fit(documents)
doc_array = count_vector.transform(documents).toarray()
freq_matrix = pd.DataFrame(doc_array, columns=count_vector.get_feature_name())
How to export a trained model
from sklearn.externals import joblib
joblib.dump(model, 'name.pkl')
# to read
model = joblib.load('name.pkl')