Cfrgtkky

Hi I am working with a difficult data set, in that the classes are both highly unbalanced and extremely uncorrelated (The set has 96,000 values, of which less than 200 are 1s.). I tried a few methods, and with each the precision and accuracy were always high, however only a few (less than 5) values are being classified as 1. I wonder if there is a way to force the machine to classify more 1s. If I could classify correctly just 25% of the time, this would be a great result.

I have tried using random forest's 'class weight' parameter, but this doesn't seem to have any effect on the result.

Thanks

import numpy as np

import pandas as pd

import sklearn as sklearn

from sklearn.tree import DecisionTreeClassifier

import matplotlib.pyplot as plt

import seaborn as sns



df = pd.read_pickle('/Users/shellyganga/Downloads/ola.pickle')



print(df.describe())



#filtering the df to improve results

df = df[(df['trip_duration'] > 5) & (df['Smooth_Driving_Score'] < 99)]



print(df.describe())





maxVal = 1

df.unsafe = df['unsafe'].where(df['unsafe'] <= maxVal, maxVal)



df.drop(df.columns[0], axis=1, inplace=True)

df.drop(df.columns[-2], axis=1, inplace=True)



#setting features and labels

labels = np.array(df['unsafe'])

features= df.drop('unsafe', axis = 1)



# Saving feature names for later use

feature_list = list(features.columns)



# Convert to numpy array

features = np.array(features)



from sklearn.model_selection import train_test_split



# 30% examples in test data

train, test, train_labels, test_labels = train_test_split(features, labels,

                                                          stratify = labels,

                                                          test_size = 0.4,

                                                          random_state = 12)



from sklearn.ensemble import RandomForestClassifier



# Create the model with 100 trees

model = RandomForestClassifier(n_estimators=100,

                               random_state=12,

                               max_features = 'sqrt',

                               n_jobs=-1, verbose = 1, class_weight={0:1, 1:1})



# Fit on training data

model.fit(train, train_labels)

predictions = model.predict(test)





print(np.mean(predictions))

print(predictions.shape)





from sklearn.metrics import classification_report

print(classification_report(test_labels, predictions)

ouput

     precision    recall  f1-score   support



          0       1.00      1.00      1.00     38300

          1       1.00      0.01      0.02        90



avg / total       1.00      1.00      1.00     38390

edit: I tried using {class_weight = 'balanced'} and provided a different result, but I am having trouble understanding it.

   micro avg       1.00      1.00      1.00     38390

   macro avg       1.00      0.51      0.51     38390

weighted avg       1.00      1.00      1.00     38390

How do I know how many positives it has predicted?