[FIXED] how to predict multiple dependent columns from 1 independent column

Issue

is it possible to predict multiple dependent columns from independent columns?

Problem Statement: I have to predict 5 factors(cEXT, cNEU,cAGR, cCON, cOPN) on the basis of STATUS column, so input variable will be STATUS column only and target variables are (cEXT, cNEU,cAGR, cCON, cOPN).

here in the above data STATUS is an independent column and cEXT, cNEU,cAGR, cCON, cOPN are the dependent columns, how can I predict those?

# independent and dependent variable split
X = df[['STATUS']]
y = df[["cEXT","cNEU","cAGR","cCON","cOPN"]]

right now I am predicting only one column so repeating the same thing 5 times so I am creating 5 models for 5 target variables.

Code:

X = df[['STATUS']]
y = df[["cEXT","cNEU","cAGR","cCON","cOPN"]]

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=5)


from sklearn.compose import ColumnTransformer
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer

ct = ColumnTransformer([
    ('step1', TfidfVectorizer(), 'STATUS')
],remainder='drop')

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, recall_score, classification_report, cohen_kappa_score
from sklearn import metrics 
from sklearn.pipeline import Pipeline

# ########## 
# RandomForest
# ##########
model = Pipeline([
        ('column_transformers', ct),
        ('model', RandomForestClassifier(criterion = 'gini', n_estimators=100, n_jobs = -1, class_weight = 'balanced', max_features = 'auto')),
    ])

# creating 5 models, can I create 1 model?
model_cEXT = model.fit(X_train, y_train['cEXT'])
model_cNEU = model.fit(X_train, y_train['cNEU'])
model_cAGR = model.fit(X_train, y_train['cAGR'])
model_cCON = model.fit(X_train, y_train['cCON'])
model_cOPN = model.fit(X_train, y_train['cOPN'])

Solution

You can use multioutput classifier from scikit-learn.

from sklearn.multioutput import MultiOutputClassifier
from sklearn.ensemble import RandomForestClassifier

clf = MultiOutputClassifier(RandomForestClassifier()).fit(X_train, y_train)
clf.predict(X_test)

Reference: Official document of MultiOutputClassifier

Answered By - Zalak Bhalani