How to plot a learning Curve in Python?

How to plot a learning Curve in Python?

How to plot a learning Curve in Python?

This recipe helps you plot a learning Curve in Python


Recipe Objective

While training a dataset sometimes we need to know how model is training with each row of data passed through it. Sometimes while training a very large dataset it takes a lots of time and for that we want to know that after passing speicific percentage of dataset what is the score of the model. So this can be done by learning curve.

This data science python source code does the following:
1. Imports Digit dataset and necessary libraries
2. Imports Learning curve function for visualization
3. Splits dataset into train and test
4. Plots graphs using matplotlib to analyze the learning curve

So this recipe is a short example of how we can plot a learning Curve in Python.

Step 1 - Import the library

import numpy as np import matplotlib.pyplot as plt from sklearn.ensemble import RandomForestClassifier from sklearn import datasets from sklearn.model_selection import learning_curve

Here we have imported various modules like datasets, RandomForestClassifier and learning_curve from differnt libraries. We will understand the use of these later while using it in the in the code snippet.
For now just have a look on these imports.

Step 2 - Setup the Data

Here we have used datasets to load the inbuilt breast cancer dataset and we have created objects X and y to store the data and the target value respectively. cancer = datasets.load_breast_cancer() X, y =,

Step 3 - Learning Curve and Scores

Here, we are using Learning curve to get train_sizes, train_score and test_score. Before using Learning Curve let us have a look on its parameters.

  • estimator: In this we have to pass the models or functions on which we want to use GridSearchCV
  • train_sizes: Relative or absolute numbers of training examples that will be used to generate the learning curve.
  • Scoring: It is used as a evaluating metric for the model performance to decide the best hyperparameters, if not especified then it uses estimator score.
  • cv : In this we have to pass a interger value, as it signifies the number of splits that is needed for cross validation. By default is set as five.
  • n_jobs : This signifies the number of jobs to be run in parallel, -1 signifies to use all processor.
train_sizes, train_scores, test_scores = learning_curve(RandomForestClassifier(), X, y, cv=10, scoring='accuracy', n_jobs=-1, train_sizes=np.linspace(0.01, 1.0, 50)) Now we have calculated the mean and standard deviation of the train and test scores. train_mean = np.mean(train_scores, axis=1) train_std = np.std(train_scores, axis=1) test_mean = np.mean(test_scores, axis=1) test_std = np.std(test_scores, axis=1)

Step 4 - Ploting the Learning Curve

Finally, its time to plot the learning curve. We have used matplotlib to plot lines and band of the learning curve. plt.subplots(1, figsize=(10,10)) plt.plot(train_sizes, train_mean, '--', color="#111111", label="Training score") plt.plot(train_sizes, test_mean, color="#111111", label="Cross-validation score") plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color="#DDDDDD") plt.fill_between(train_sizes, test_mean - test_std, test_mean + test_std, color="#DDDDDD") plt.title("Learning Curve") plt.xlabel("Training Set Size"), plt.ylabel("Accuracy Score"), plt.legend(loc="best") plt.tight_layout() As an output we get:


Relevant Projects

Data Science Project-TalkingData AdTracking Fraud Detection
Machine Learning Project in R-Detect fraudulent click traffic for mobile app ads using R data science programming language.

PySpark Tutorial - Learn to use Apache Spark with Python
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.

Perform Time series modelling using Facebook Prophet
In this project, we are going to talk about Time Series Forecasting to predict the electricity requirement for a particular house using Prophet.

Credit Card Fraud Detection as a Classification Problem
In this data science project, we will predict the credit card fraud in the transactional dataset using some of the predictive models.

Human Activity Recognition Using Smartphones Data Set
In this deep learning project, you will build a classification system where to precisely identify human fitness activities.

Time Series Forecasting with LSTM Neural Network Python
Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.

Predict Credit Default | Give Me Some Credit Kaggle
In this data science project, you will predict borrowers chance of defaulting on credit loans by building a credit score prediction model.

Loan Eligibility Prediction using Gradient Boosting Classifier
This data science in python project predicts if a loan should be given to an applicant or not. We predict if the customer is eligible for loan based on several factors like credit score and past history.

Learn to prepare data for your next machine learning project
Text data requires special preparation before you can start using it for any machine learning project.In this ML project, you will learn about applying Machine Learning models to create classifiers and learn how to make sense of textual data.

Natural language processing Chatbot application using NLTK for text classification
In this NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.