ngmars

import joblib
import multiprocessing as mp
from joblib import load

#add the path of the file you wish to load
filename= './new_model.sav'
#your models weights are now stored in the variable loaded model
loaded_model = load("new_model.sav")

#define a function and pass your parameters 

ngmars

import joblib
import multiprocessing as mp
from joblib import load

#add the path of the file you wish to load
filename= './new_model.sav'
#your models weights are now stored in the variable loaded model
loaded_model = load("new_model.sav")

#define a function and pass your parameters 

ngmars

import joblib
import multiprocessing as mp

clf.fit(y_train, X_train)
weight = 'new_model.sav'
joblib.dump(clf, weight)


#define a function and pass your parameters 
def predict(A):

ngmars

import joblib
clf.fit(y_train, X_train)
weight = 'new_model.sav'
joblib.dump(clf, weight)

#define a function and pass your parameters 
def predict(A,B,C):
  arr=[A,B,C]
  y_test=[arr]
  y_test=np.asarray(y_test)

ngmars

from joblib import load

#add the path of the file you wish to load
filename= './new_model.sav'

#your models weights are now stored in the variable loaded model
loaded_model = load("new_model.sav")

ngmars

import joblib

#fitting the clf variable to the training data
clf.fit(y_train, X_train)

#saving weights into a .sav file
weight = 'new_model.sav'
joblib.dump(clf, weight)