How to plot learning curves in scikit-learn Python

A learning curve sklearn refers to the graphical representation that illustrates the learning rate. It demonstrates how an individual's performance improves as they gain experience.  

A learning curve is a graph with the x-axis representing time. The y-axis represents performance or skill. The curve starts at a lower level, indicating a slower initial rate of progress. It becomes steeper as learning occurs. As time goes on, the rate of improvement may plateau as the individual or group reaches a level of mastery. The complexity of learning depends on the skill and the learner's commitment.   

Understanding the learning curve is important when adopting new technology. It helps manage expectations and provides insight into the time and effort required. The progress may be slow and frustrating as one grapples with unfamiliar concepts.   

Learning curves describe the rate at which learning acquisition occurs over time. They can vary in shape, indicating different patterns of progress. Two common types of learning curves are the steep learning curve and the flat learning curve.   

• Steep Learning Curve: It represents a rapid learning or skill improvement rate. Progress is quick, and individuals grasp concepts or get skills. As they invest time and effort, they experience significant advancements. However, as you keep learning, progress may slow down, showing less benefit. When motivated, learners often experience this type of learning curve.  

• Flat Learning Curve: It signifies a slower learning or skill development rate. Progress is gradual, and individuals may struggle to grasp concepts or get new skills. They may need more time, practice, or more resources to improve. Learners face challenges and experience this type of learning curve.  

Some various theories and models propose different types of learning styles. The two discussed learning styles are active learning and reflective learning. Let's explore each of them in more detail:   

• Active Learning: Active learning emphasizes engagement and hands-on participation in the learning process. It involves manipulating and interacting with information and concepts to deepen understanding.   
• Reflective Learning: It focuses on internalizing and processing information through reflection and introspection. They prefer to think about the material and analyze thoughts and experiences.  

The cross-validation generator splits the dataset into k parts. We average the scores over all k runs for the training subset. To analyze the learning curve, we typically employ cross-validation. This helps us determine scores for various training set sizes. The learning curve is important because it helps us understand the bias-variance trade-off. We will now learn how to use learning curves in Python using the scikit-learn library of Python. The learning curve measures a model's performance on the training set and cross-validation. We will use subsets of the training set with varying sizes to train the estimator. We will compute a score for each training subset size and the test set. We'd get two learning curves looking at these if we plotted the error scores for each training size. Supervised learning models have bias and variance shown in learning curves. If the estimator can learn bit by bit, it will speed up fitting for various training set sizes.   

The orange dashed line denotes the training accuracy of the model in the plot. The blue line denotes the validation accuracy of the model. The black dashed line denotes the desired model accuracy. To send advertising, creating user profiles requires technical storage or access.  

Here is an example of how to plot learning curves in scikit-learn Python:   

Fig: Preview of the output that you will get on running this code from your IDE.

Code

In this solution, we are using scikit-learn, NumPy, and Matplotlib.

Instructions

Follow the steps carefully to get the output easily.

1. Install PyCharm Community Edition on your computer.
2. Open the terminal and install the required libraries with the following commands.
3. Install Scikit-learn - pip install scikit-learn.
4. Install NumPy - pip install numpy.
5. Install Matplotlib - pip install matplotlib.
6. Create a new Python file(e.g. test.py).
7. Copy the snippet using the 'copy' button and paste it into that file.
8. Run the file using the run button.

I hope you found this useful. I have added the link to dependent libraries, and version information in the following sections.

I found this code snippet by searching for 'How to plot multiple learning curve from different model on the same graph' in kandi. You can try any such use case!

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

1. The solution is created in PyCharm 2022.3.3.
2. The solution is tested on Python 3.9.7.
3. Scikit-learn version 1.2.2.
4. NumPy version v1.24.2.
5. Matplotlib version v3.7.1.

Using this solution, we are able to plot learning curves using sci-kit-learn Python with simple steps. This process also facilitates an easy-to-use, hassle-free method to create a hands-on working version of code which would help us to plot learning curves using scikit-learn in Python.

Dependent Libraries

You can also search for any dependent libraries on kandi like 'scikit-learn', 'matplotlib', and 'NumPy'.

FAQ:   

1. What are Plotting Learning Curves, and how does it work?   

During training, the model visually shows its performance. By examining the amount of training data, we can see how the model's performance changes. It can also help identify problems like overfitting or underfitting.   

Here's how it works:   

• Data Splitting.   
• Model Training and Evaluation.   
• Plotting the Learning Curve.   
• Interpreting the Curve.   
• Using Learning Curves for Improvement.   

2. What is the difference between a training score curve and a validation score curve?   

Here's the difference between the two:   

• Training Score Curve: It shows how the model performs as training goes on. We plot the model's performance on the y-axis. The number of training iterations plots it. The curve shows how the model's performance changes over time as it learns from the training data.   
• Validation Score Curve: The model's performance on a separate validation dataset differs from the training data. The model uses it to test its generalization ability.   

3. How does scikit-learn version 0.21 determine cross-validated training and test scores?   

Cross-validation is a way to check how well a model works with new data. The process involves splitting the dataset into many subsets or folds. We train the model on the part of the data (training set) and then evaluate it on the remaining fold (test set). We repeat this process for each fold and aggregate the scores obtained.  

4. How do you choose the right training set sizes for your Machine Learning Projects?   

The number of examples needed for training depends on the size of the dataset and computer power.   

5. What does sklearn use Support Vector Machines (SVMs) for in learning curves?   

SVMs are algorithms that can work with the learning_curve function. You can use SVM models to classify and predict data with guidance. They are particularly effective in high-dimensional spaces.  

Support

1. For any support on kandi solution kits, please use the chat
2. For further learning resources, visit the Open Weaver Community learning page.