So just replace your line like -
y_score = classifier.fit(X_train, y_train).predict_proba(X_test)
or
y_score = classifier.fit(X_train, y_train).predict(X_test)
Just to mention RFC do supports anycodings_scikit-learn oob_decision_function, which is the out anycodings_scikit-learn of bag estimate on your training set.,AttributeError: Base estimator doesn't have anycodings_scikit-learn a decision_function attribute.,Rule based named entity recognizer without parts of speech label or any other information,Here is the reference for the supported anycodings_scikit-learn functions
I can't see how to transform this part of anycodings_scikit-learn the code
# Learn to predict each class against the other classifier = OneVsRestClassifier(svm.SVC(kernel = 'linear', probability = True, random_state = random_state)) y_score = classifier.fit(X_train, y_train).decision_function(X_test)
I tried
# Learn to predict each class against the other classifier = OneVsRestClassifier(RandomForestClassifier()) y_score = classifier.fit(X_train, y_train).decision_function(X_test)
So just replace your line like -
y_score = classifier.fit(X_train, y_train).predict_proba(X_test)
or
y_score = classifier.fit(X_train, y_train).predict(X_test)
Most real world machine learning applications are based on multi-class Classification algorithms (ie. Object Detection, Natural Language Processing, Product Recommendations).,A good multi-class classification machine learning algorithm involves the following steps:,Numpy: The library used for scientific computing. Here we are using the function vectorize for reversing the factorization of our classes to text.,We are going to observe the importance for each of the features and then store the Random Forest classifier using the joblib function of sklearn.
- Pandas: One of the most popular libraries for data manipulation and storage. This is used to read/write the dataset and store it in a dataframe object. The library also provides various methods for dataframe transformation.
- Numpy: The library used for scientific computing. Here we are using the function vectorize for reversing the factorization of our classes to text.
- Sklearn: The library is used for a wide variety of tasks, i.e. dataset splitting into test and train, training the random forest, and creating the confusion matrix.
#Importing Libraries
import numpy as np
import pandas as pd
from sklearn.model_selection
import train_test_split
from sklearn.preprocessing
import StandardScaler
from sklearn.ensemble
import RandomForestClassifier
from sklearn.metrics
import confusion_matrix
from sklearn.externals
import joblib
print('Libraries Imported')- Store the data without colnames in dataframe named 'dataset'.
- Rename the columns to ['sepal length in cm', 'sepal width in cm','petal length in cm','petal width in cm','species'].
- Show the first five records of the dataset.
#Creating Dataset and including the first row by setting no header as input
dataset = pd.read_csv('iris.data.csv', header = None)
#Renaming the columns
dataset.columns = ['sepal length in cm', 'sepal width in cm', 'petal length in cm', 'petal width in cm', 'species']
print('Shape of the dataset: ' + str(dataset.shape))
print(dataset.head())- Use pandas factorize function to factorize the species column in the dataset. This will create both factors and the definitions for the factors.
- Store the factorized column as species.
- Store the definitions for the factors.
- Show the first five rows for the species column and the defintions array.
#Creating the dependent variable class
factor = pd.factorize(dataset['species'])
dataset.species = factor[0]
definitions = factor[1]
print(dataset.species.head())
print(definitions)The below code uses the prebuilt function 'train_test_split' in a sklearn library for creating the train and test arrays for both independent and dependent variable. Also, random_state = 21 is assigned for random distribution of data.
# Creating the Training and Test set from data X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 21)
- Define a scaler by calling the function from sklearn library.
- Transform train feature dataset (X_train) and fit the scaler on train feature dataset.
- Use the scaler to transform test feature dataset (X_test).
# Feature Scaling scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test)
We will use a Support Vector Machine, which is a binary classification algorithm and use it with the One-vs-Rest heuristic to perform multi-class classification.,Conclusion:Now that you know how to use the One-vs-Rest heuristic method for performing multi-class classification with binary classifiers, you can try using it next time you have to perform some multi-class classification task.,One-vs-Rest (OVR) Method:Many popular classification algorithms were designed natively for binary classification problems. These algorithms include :,Classification is perhaps the most common Machine Learning task. Before we jump into what One-vs-Rest (OVR) classifiers are and how they work, you may follow the link below and get a brief overview of what classification is and how it is useful.
Output:
Test Set Accuracy: 66.66666666666666 %
Classification Report:
precision recall f1 - score support
0 0.62 1.00 0.77 5
1 0.70 0.88 0.78 8
micro avg 0.67 0.92 0.77 13
macro avg 0.66 0.94 0.77 13
weighted avg 0.67 0.92 0.77 13