What is Instance-Based Learning

k-Nearest Neighbor Learning and Locally Weighted Regression 

Instance-Based Learning Topics

• Introduction

• k -Nearest Neighbor Learning

• Locally Weighted Regression

• Radial Basis Functions

• Case-Based Reasoning

• Remarks on Lazy and Eager Learning.

What is Instance-based Learning?

Instance-based learning is a type of machine learning that uses a lazy learning approach, where the algorithm stores the training examples and makes predictions based on similarity measures between the new instance and the stored instances. 

Introduction

Instance-Based Learning is a type of machine learning where the algorithm is given a dataset, and it learns by memorizing the instances in the dataset. The algorithm uses these instances to make predictions on new, unseen instances. The key idea behind instance-based learning is that the function approximator only makes predictions based on the similarity between the new instance and the instances in the dataset.

There are several instance-based learning techniques, including:

k-Nearest Neighbor Learning:

The k-Nearest Neighbor (k-NN) algorithm is a type of instance-based learning, where the algorithm stores the entire dataset and makes predictions by finding the k closest instances to the new instance and taking the majority vote of their labels. The user selects the value of the hyperparameter k.

Algorithm steps for the k-NN algorithm:

• Collect the training data, which consists of a set of labelled instances.

• Define a distance metric that measures the similarity between two instances. Euclidean distance, Manhattan distance, and cosine similarity are examples of common distance measures.

• Given a new, unlabeled instance, compute the distances between the new instance and all instances in the training data.

• Select the k nearest neighbours to the new instance based on the computed distances.

• Assign the new instance to the class that is most common among its k nearest neighbours. If there is a tie, randomly assign it to one of the tied classes.

Example Python code for the k-NN algorithm using the scikit-learn library:

python code

from sklearn.neighbors import KNeighborsClassifier

from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score

# Load the iris dataset

iris = load_iris()

# Split the dataset into training and test sets

X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3, random_state=42)

# Create a k-NN classifier with k=3

knn = KNeighborsClassifier(n_neighbors=3)

# Fit the classifier to the training data

knn.fit(X_train, y_train)

# Use the classifier to make predictions on the test data

y_pred = knn.predict(X_test)

# Compute the accuracy of the classifier

accuracy = accuracy_score(y_test, y_pred)

print("Accuracy: {:.2f}".format(accuracy))

In this example, we're using the k-NN algorithm to classify the iris dataset. We split the dataset into training and test sets, create a k-NN classifier with k=3, fit the classifier to the training data, use the classifier to make predictions on the test data and compute the accuracy of the classifier.

Locally Weighted Regression:

Locally Weighted Regression (LWR) is a type of instance-based learning where the algorithm stores the entire dataset and makes predictions by fitting a regression model to the k-nearest neighbours of the new instance, where the weight of each neighbour is determined by a distance metric.

Radial Basis Functions:

Radial Basis Functions (RBF) is a type of instance-based learning, where the algorithm stores the entire dataset and makes predictions by fitting a function to the k-nearest neighbours of the new instance using a radial basis function.

Case-Based Reasoning:

Case-Based Reasoning (CBR) is a type of instance-based learning where the algorithm stores the entire dataset as cases, where each case consists of an input and a corresponding output. When a new input is given, the algorithm searches for a similar case in the dataset and returns the output of that case.

Remarks on Lazy and Eager Learning:

k-NN, LWR and RBF are considered lazy learning algorithms because they do not learn a model from the training data. Instead, they simply store the training data and use it for prediction. On the other hand, CBR is considered an eager learning algorithm because it learns a model from the training data and uses it for prediction.

Lazy learning algorithms have the advantage of being able to handle non-linear relationships between the input and output, but they can be slow and memory-intensive because they store the entire dataset. Eager learning algorithms are faster and use less memory, but they can struggle with non-linear relationships between the input and output.

Overall, instance-based learning techniques are useful when the relationship between the input and output is complex and non-linear, but they can be computationally expensive and require significant amounts of memory.

IBL stands for "Instance-Based Learning," which is a machine learning approach that focuses on learning from specific examples or instances in the training data rather than trying to learn a more general rule or model. One of the most popular IBL algorithms is called k-Nearest Neighbors (k-NN).

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