Use the loaded model to make predictions print(knn_from_joblib.
Load the model from the file knn_from_joblib = joblib.load('my_model_') Save the model from sklearn.externals import joblib joblib.dump(knn, 'my_model_') Joblib.dump to serialize an object hierarchy joblib.load to deserialize a data stream Prediction: # Prediction print("Before save:",knn.predict(X_test)) Train: # train model knn.fit(X_train, y_train) Split the dataset into train and test: # Split the dataset into train and test from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test =train_test_split(X, y, test_size = 0.3, random_state = 2020)īuild a good model: # import KNN model from sklearn.neighbors import KNeighborsClassifier as KNN knn = KNN(n_neighbors = 5) it also helps to transfer your model to someone who wants to make predictions.ĭata and target: f(x)=y #Load Library import numpy as np # Load dataset from sklearn.datasets import load_iris iris = load_iris() X = iris.data y = iris.target When we need the same trained data in some different projects or later sometime, to avoid. Also, we deal with different types and sizes of data. This article explains how parallel processing works and examples of its application in real-world use cases. The saving of data is called Serialization while restoring the data is called Deserialization. Parallel processing is a computing technique when multiple streams of calculations or data processing tasks co-occur through numerous central processing units (CPUs) working concurrently.
It also helps to compare the models with other models. It’s important to save your model for future use to make a prediction on unseen data. Imagine when you forget to save the best model for you in time and require long processing. Save and Load Machine Learning Models with joblib in Python - KNeighborsClassifier
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