from flask import Flask , request import numpy as np import pickle import pandas as pd import flasgger from flasgger import Swagger app = Flask ( __name__ ) Swagger ( app ) pickle_in = open ( "classifier.pkl" , "rb" ) classifier = pickle . load ( pickle_in ) @ app . route ( '/' ) def welcome (): return "Welcome All" @ app . route ( '/predict' , methods = [ "Get" ]) def predict_note_authentication (): """Let's Authenticate the Banks Note This is using docstrings for specifications. --- parameters: - name: variance in: query type: number required: true - name: skewness in: query type: number required: true - name: curtosis in: query type: number required: true - name: entropy in: query type: number required: true responses: 200: ...
In scikit-learn, a lot of classifiers come with a built-in method of handling imbalanced classes. If we have highly imbalanced classes and have not addressed it during preprocessing, we have the option of using the class_weight parameter to weight the classes to make certain we have a balanced mix of each class. Specifically, the balanced argument will automatically weigh classes inversely proportional to their frequency. import numpy as np import pandas as pd import seaborn as sns import warnings from imblearn.over_sampling import SMOTE from imblearn.pipeline import make_pipeline from pylab import rcParams from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score from sklearn.metrics import precision_score , recall_score from sklearn.metrics import f1_score , roc_auc_score , roc_curve from sklearn.model_selection import train_test_split from sklearn.model_selection import GridSearchCV In [2]:...