Machine Learning Quick Reference

Model building count vectorization

For building count vectorization we can split the data into train and test dataset as follows:

from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.metrics import f1_score,accuracy_score # splitting data into training and validation set xtrain, xtest, ytrain, ytest = train_test_split(bow, Newdata['label'], random_state=42, test_size=0.3) lreg = LogisticRegression() lreg.fit(xtrain, ytrain) # training the model prediction = lreg.predict_proba(xtest) # predicting on the validation set prediction_int = prediction[:,1] >= 0.3 # if prediction is greater than or equal to 0.3 than 1 else 0 prediction_int = prediction_int.astype(np.int) print("F1 Score-",f1_score(ytest, ...