Instance-Based vs Model-Based Machine Learning: Key Differences Explained

LEARNING: We humans learn from our daily experiences in the same way machine can learn with data. There are two ways of learning for machines. They are:

• Memorizing (Instance-Based Machine Learning)

• Generalizing (Model-Based Machine Learning)

Instance-Based Machine Learning

Instance-based learning, also known as Memory-based learning or Lazy learning, refers to techniques that instantly produce class labels or predictions for classification and regression. This is based on the similarity of data points to their nearest neighbor(s) in the training set. In laymen terms this method stores the dataset of examples predictions of new data is made by using the stored dataset.

Example:

In a k-nearest neighbors algorithm, the model would store a dataset of instances (also called “training examples”) and, when given a new data point, it would find the k instances in the dataset that are most similar to the new data point and use those to make a prediction.

• Complexity of the algorithm is directly proportional to size of the training data.

• Instance-based learning differs from techniques.

Some of the instance-based learning algorithms are :

- K Nearest Neighbor (KNN)

- Self-Organizing Map (SOM)

- Learning Vector Quantization (LVQ)

- Locally Weighted Learning (LWL**)**

Model-Based Learning

In model-based learning, predictions are made using a mathematical model that is created. Typically the model is a function which takes input data and produces output based on the internal parameters of the model. In other words, relationships between inputs and outputs are calculated, and a generalized function is created. New input data is then fed into the function to predict the outputs. The goal of a model-based algorithm is to generalize to new examples. To achieve this, model-based algorithms search for the best values for the model’s parameters, often called theta. This process of searching, or "learning," is the essence of machine learning.

Example:

A linear regression model takes in input data and makes a prediction based on a linear equation that is fit to the data.

The introduction of model-based learning emerged from the combination of three key ideas:

• The adoption of a Bayesian viewpoint.

• the use of factor graphs (a type of probabilistic graphical model).

• the application of fast, deterministic, efficient, and approximate inference algorithms.

The main idea is to make all assumptions about the problem clear through a model. In this setup, a model is a set of assumptions about the world shown in a probabilistic graphical format, with all parameters and variables treated as random components.

Stages of MBML(Model-Based Machine Learning)

There are 3 steps to model-based machine learning, namely:

• Describe the Model: Explain how the data was generated using factor graphs.

• Condition on Observed Data: Set the observed variables to their known values.

• Perform Inference: Use backward reasoning to update the prior distribution of the hidden variables or parameters. In simple terms, calculate the posterior probability distributions of hidden variables based on the observed variables.

MODEL: It is a set of Parameters.

Examples: Naive Bayes(Probability), Neural Network(Weight), Linear Regression(slope and intercepts)

Conclusion

In conclusion, both instance-based and model-based machine learning approaches have their own strengths and limitations.

Instance-based methods can be very effective for making predictions, but they can be computationally expensive and may require a lot of memory to store the dataset. Model-based methods, on the other hand, can be more efficient and may be able to make more accurate predictions, but they can be more prone to overfitting, which is when the model is too closely tied to the training data and does not generalize well to new data.