Machine Learning Fundamentals: Cross Validation
Summary
TLDRIn this StatQuest video, Josh Starmer explains cross-validation, a technique for comparing different machine learning methods to predict heart disease using variables like chest pain and blood circulation. The video illustrates the importance of not using all data for training to avoid overfitting and introduces the concept of dividing data into blocks for training and testing, exemplified by four-fold cross-validation. It also touches on using cross-validation to find the best tuning parameters, like in Ridge regression, and concludes with an invitation to subscribe for more content.
Takeaways
- 📚 Cross-validation is a technique used to compare different machine learning methods and assess their performance in practice.
- 🔍 The purpose of using variables like chest pain and blood circulation is to predict heart disease, which is the main focus of the data in the script.
- 🤖 Machine learning methods such as logistic regression, K nearest neighbors, and support vector machines are potential options for analysis.
- 🚫 Using all data for training and no data for testing is not advisable as it doesn't allow for method evaluation on unseen data.
- 📈 Splitting data into training and testing sets, such as 75% for training and 25% for testing, is a common approach but not always optimal.
- 🔄 Cross-validation systematically uses all subsets of data for both training and testing to ensure a fair evaluation of the machine learning methods.
- 📉 In the provided example, four-fold cross-validation is used, but the number of folds can vary based on the dataset and the analysis needs.
- 👉 'Leave One Out Cross Validation' is an extreme form of cross-validation where each sample is used for testing once and the rest for training.
- 🔢 10-fold cross-validation is a common practice that divides the data into ten parts, using nine for training and one for testing in each iteration.
- 🛠 Cross-validation can also help in tuning hyperparameters of machine learning models, such as the tuning parameter in Ridge regression.
- 🎓 The script is educational, aiming to teach viewers about cross-validation through an example and encouraging them to subscribe for more content.
Q & A
What is the main topic discussed in the StatQuest video?
-The main topic discussed in the video is cross-validation, a technique used in machine learning to compare and evaluate different machine learning methods.
What are the variables mentioned for predicting heart disease in the script?
-The variables mentioned for predicting heart disease include chest pain and good blood circulation.
What is the purpose of training a machine learning algorithm?
-The purpose of training a machine learning algorithm is to estimate its parameters using some of the available data, which helps in learning the underlying patterns in the data.
Why is it not ideal to use the same data for both training and testing a machine learning model?
-Using the same data for both training and testing is not ideal because it does not provide an unbiased evaluation of the model's performance on new, unseen data.
What is the basic idea behind cross-validation?
-The basic idea behind cross-validation is to use different subsets of the data for training and testing the machine learning model in a way that every data point gets to be in a test set exactly once.
What is the term used for dividing the data into four parts for cross-validation?
-The term used for dividing the data into four parts for cross-validation is four-fold cross-validation.
What is the term for the cross-validation technique where each individual sample is used as a test set?
-The term for the cross-validation technique where each individual sample is used as a test set is 'Leave One Out Cross Validation'.
How many blocks are typically used in 10-fold cross-validation?
-In 10-fold cross-validation, the data is divided into 10 blocks or subsets.
What is a tuning parameter in the context of machine learning?
-A tuning parameter in machine learning is a parameter that is not estimated from the data but is set by the user or found through techniques like cross-validation to optimize the model's performance.
How can cross-validation help in finding the best value for a tuning parameter?
-Cross-validation can help in finding the best value for a tuning parameter by systematically testing different values and evaluating the model's performance for each, thus identifying the value that yields the best results.
What is the final decision made in the script based on the cross-validation results?
-The final decision made in the script is to use the support vector machine method for classification, as it performed the best in classifying the test data sets during cross-validation.
Outlines
🧑🏫 Introduction to Cross-Validation
Josh Starmer from StatQuest introduces the concept of cross-validation in machine learning. The video begins with a discussion on using various variables like chest pain and blood circulation to predict heart disease. It explains the need to choose an appropriate machine learning method, such as logistic regression, K-nearest neighbors, or support vector machines. Cross-validation is presented as a method to compare these machine learning methods to determine their effectiveness. The script outlines the importance of using data to both train and test machine learning algorithms, emphasizing that using the same data for both could lead to overfitting. It introduces the idea of dividing the data into blocks and using different combinations for training and testing to get a robust evaluation of the algorithms. The example of four-fold cross-validation is given, where the data is divided into four blocks, and each block is used once for testing while the others are used for training. The paragraph concludes with a decision to use the support vector machine based on its performance in classifying the test data.
🔍 Advanced Cross-Validation Techniques
In the second paragraph, the video script delves into advanced uses of cross-validation. It mentions the scenario where a machine learning method has a tuning parameter that needs to be optimized. An example given is Ridge regression, which has a tuning parameter that isn't estimated by the algorithm but is set by the user. The script explains how 10-fold cross-validation can be used to find the optimal value for such tuning parameters. The video ends with an invitation for viewers to subscribe for more content, to like the video, and to consider purchasing original songs by the presenter, Josh Starmer. The script wraps up with a light-hearted 'Double BAM!!!' and 'Tiny BAM!' to emphasize the completion of the tutorial and the excitement around the topic.
Mindmap
Keywords
💡Cross-validation
💡Machine Learning
💡Logistic Regression
💡K-Nearest Neighbors (KNN)
💡Support Vector Machines (SVM)
💡Parameters
💡Training
💡Testing
💡Four-fold Cross Validation
💡Leave One Out Cross Validation
💡10-fold Cross Validation
💡Tuning Parameter
Highlights
Introduction to cross-validation in machine learning by StatQuest with Josh Starmer.
Cross-validation helps decide the best machine learning method for a given dataset.
Data variables such as chest pain and blood circulation are used to predict heart disease.
Different machine learning methods like logistic regression, K nearest neighbors, and support vector machines are discussed.
The importance of training and testing algorithms to estimate parameters and evaluate performance.
Avoiding the use of all data for training to prevent lack of data for testing.
Using a fixed split of data for training and testing may not always be the best approach.
Cross-validation uses all data blocks one at a time for testing to ensure robust evaluation.
Four-fold cross-validation as an example of dividing data into blocks for validation.
The concept of 'Leave One Out Cross Validation' where each sample is used for testing individually.
10-fold cross-validation as a common practice in machine learning.
Cross-validation can also be used to find the best value for tuning parameters in algorithms.
Support vector machines being chosen as the best method for classifying test data sets in the example.
The practical application of cross-validation in choosing and tuning machine learning models.
StatQuest encourages viewers to subscribe for more educational content.
Invitation to support StatQuest by liking the video and considering purchasing original songs.
Transcripts
StatQuest
Check it out
talking about
Machine-learning. Yeah StatQuest
Check it out
Talking about cross-validation
StatQuest
Hello, I'm Josh stormer and welcome to StatQuest today we're going to talk about cross validation and it's gonna be clearly explained
Okay, let's start with some data
We want to use the variables chest pain good blood circulation
Etc
To predict if someone has heart disease
Then when a new patient shows up
we can measure these variables and
Predict if they have heart disease or not
However, first we have to decide which machine learning method would be best
we could use logistic regression or
K nearest neighbors
Or support vector machines and
Many more machine learning methods. How do we decide which one to use?
Cross-validation allows us to compare different machine learning methods and get a sense of how well they will work in practice
Imagine that this blue column represented all of the data that we have collected about people with and without heart disease
We need to do two things with this data
One we need to estimate the parameters for the machine learning methods in
In other words to use logistic regression we have to use some of the data to estimate the shape of this curve
in machine learning lingo
Estimating parameters is called training the algorithm
The second thing we need to do with this data is evaluate how well the machine learning methods work in?
Other words we need to find out if this curve will do a good job categorizing new data in
In machine learning lingo
Evaluating a method is called testing the algorithm
Thus using machine learning lingo we need the data to
one train the machine learning methods and
to test the machine learning methods a
A terrible approach would be to use all the data to estimate the parameters ie to train the algorithm
Because then we wouldn't have any data left to test the method
Reusing the same data for both training and
Testing is a bad idea because we need to know how the method will work on data. It wasn't trained on a
Slightly better idea would be to use the first seventy-five percent of the data for training and the last 25% of the data for testing
We could then compare methods by seeing how well each one categorized the test data
But how do we know that using the first?
Seventy-five percent of the data for training in the last 25% of the data for testing is the best way to divide up the data
What if we use the first 25% of the data for testing
Or what about one of these middle blocks?
Rather than worry too much about which block would be best for testing cross-validation uses them all one at a time and summarizes the results at the end
For example cross-validation would start by using the first three blocks to train the method and
then use the last block to test the method and
Then it keeps track of how well the method did with the test data
then it uses this combination of blocks to train the method and
this block is used for testing and
Then it keeps track of how well the method did with the test data, etc
Etc, etc
in the end every block of data is used for testing and we can compare methods by seeing how well they performed in
This case since the support vector machine did the best job classifying the test data sets. We'll use it
BAM!!!
Note: in this example, we divided the data into 4 blocks. This is called four-fold cross validation
However, the number of blocks is arbitrary
In an extreme case we could call each individual patient (or sample) a block
This is called "Leave One Out Cross Validation"
Each sample is tested individually
That said in practice it is very common to divide the data into ten blocks. This is called 10-fold cross-validation
Double BAM!!!
One last note before we're done
Say like we wanted to use a method that involved a tuning parameter a parameter that isn't estimated but is just sort of guessed
For example Ridge regression has a tuning parameter
Then we could use 10-fold cross validation
to help find the best value for that tuning parameter
Tiny Bam!
Hooray we've made it to the end of another exciting StatQuest if you like this StatQuest and want to see more please subscribe
And if you want to support StatQuest well
Please click the like button down below and consider buying one of my original songs
Alright until next time quest on
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