Lazy Learning vs. Eager Learning Algorithms in Machine Learning
In machine learning, it is essential to understand the algorithm’s working principle and primary classification of the same for avoiding misconceptions and other errors related to the same. There are mainly two types of machine learning algorithms, lazy and eager learning algorithms, based on their training style and other principles. A proper machine learning algorithm should be selected according to the problem statement for a better-performing model.
This article will discuss the lazy and eager learning algorithms with their core intuition, working mechanisms, advantages, and disadvantages. We will also discuss the best-fit model selection according to the data type and requirements of the model. These concepts will help understand the algorithm’s classification better and help understand some common and must-know properties of both types.
After going through this article, you will learn the following:
- The core idea of lazy learning and eager learning algorithms
- How the lazy and eager learning algorithms work?
- Difference between both the techniques
This article was published as a part of the Data Science Blogathon.
Table of Contents
- Lazy Learning Algorithms: How Does it Work?
- Eager Learning Algorithms: How Does It Work?
- Lazy vs. Eager Learning: The Difference
- Which One is Better for You?
Lazy Learning Algorithms: How Does it Work?
Generally, in the case of traditional machine learning algorithms, the data is acquired and fed to the algorithm. The algorithm trains on the data based on its working mechanisms and returns a trained model that will now be able to predict unseen datasets and will predict with some accuracy.
Lazy learning algorithms are slightly different from traditional machine learning algorithms. In the lazy learning algorithm, the working mechanism of the algorithm does not change; it remains the same, but the process of gaining data and using it further changes for algorithms.
In lazy learning algorithms, while training, the model takes the data as input but does not train on it, meaning that the algorithm will store the data and do nothing with it. When the time comes for the prediction, the algorithm uses the stored data, applies all the operations and techniques per its working mechanism, and returns an output.
Lazy learning algorithms are also lazy evaluation algorithms as they evaluate the data very lazily. One of the most famous examples of lazy learning algorithms is K nearest neighbors algorithm which generally stores the data. At the same time, the training phase works on the data, while the prediction or the testing phase.
Let us try to understand the process with an example. Suppose you have a dataset and will use the KNN algorithm for model building. Now, while training and fitting the model, the algorithm will accept and store the dataset without doing anything. When the testing phase comes, and you query a datapoint from the algorithm for the prediction, as per KNN’s working mechanism, the algorithm will calculate the nearest neighbors of the datapoint and return the output.
Here, note that as the model is just storing the data during the training phase, the training time for this algorithm is significantly less. In contrast, the model does all the calculations while the testing phase, making the prediction phase slower and taking higher times for predictions.
Eager Learning Algorithms: How Does it Work?
Eager learning algorithms are just like other traditional machine learning algorithms, which take the data as input and process it while the training phase only. When the prediction phase comes, the model will be ready to predict as per its learnings and observations from the training data.
Algorithms like Linear regression, logistic regression, support vector machines, decision tree artificial neural networks, etc., are examples of eager learning algorithms.
Let us try to understand the process of eager learning algorithms with an example. Suppose you have a dataset and will use linear regression for the model building. Simple, as per the other machine learning algorithms, the model will take the data as input and will try to process it while the training phase only; it will apply all the functions and other techniques associated with the linear regression algorithm and will output a trained and knowledgable model after training. Now in the prediction phase, where you query a point for prediction, the model will give instant results according to its learning and training from the fed data while training.
Here note that the learning algorithms process the data while the training phase only makes the algorithm’s training slower. Still, the predictions from the algorithms are very fast compared to lazy learning algorithms.
Lazy vs. Eager Learning Algorithms: The Difference
|Property||Lazy Learning||Eager Learning|
|Training Speed||Fast, stores the data while training||Slow, Tries to learn from data while training|
|Prediction Speed||Too Slow tries to apply functions and learnings in the prediction stage||Faster, predicts very fast as there are pre-defined functions|
|Learning Scope||Medium, it can learn from data while training||Medium, it can learn from data while testing|
|Pre Calculated Algorithm||Absent, calculations are done while the testing phase||At present, here calculations are already done in the training phase|
Which One is Best for You?
After this whole discussion, a question might come to your mind which approach is better and which should be used when?
The answer to this question is in only two words: situation based. As we can not control our data with the algorithm and we cannot changes, we can change the algorithm as per the data and its variations, and that is where the answer to this question lies.
Everything depends on the type of data, its patterns, what kind of model you want, and your requirements. Sometimes, it is essential to train the algorithm faster in an emergency; the lazy learning approach is reasonable. Sometimes it is okay for us to train the model for a longer time to make it faster while prediction than the enthusiastic learning approach is reasonable.
In some of the datasets, the behavior of the data matches very correctly to some of the lazy learning approaches, and you also want a fast predictor model; in such cases, you can use sluggish learning methods and apply some other techniques or tune the algorithm in such a way that model becomes less complex and takes less time to predict.
In this article, we discussed the lazy and eager learning algorithms in machine learning with core intuition and ideas behind them with suitable examples. We also discussed an approach for selecting the best-fit algorithm according to the problem statement. This will help one to identify the algorithm, classify them, and use them correctly.
Some of the Key Takeaways from this article are:
Lazy learning algorithms are types of algorithms that store the data while training and preprocessing it during the testing phase.
Lazy learning algorithms take a shorter time for training and a longer time for predicting.
The eager learning algorithm processes the data while the training phase is only.
Eager learning algorithms are faster than lazy learning algorithms for predicting data observations.
A proper approach should be selected according to the model’s data type and requirements.
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