Thakshashila: /* SEO Keywords */

2025-06-10T06:20:55Z

SEO Keywords

← Older revision		Revision as of 06:20, 10 June 2025
Line 75:		Line 75:

	classification in machine learning, types of classification, binary classification, multi-class classification, supervised learning classification, classification algorithms, classification examples, machine learning tasks		classification in machine learning, types of classification, binary classification, multi-class classification, supervised learning classification, classification algorithms, classification examples, machine learning tasks
			[[Category:Artificial Intelligence]]

Thakshashila: Created page with "= Classification = '''Classification''' is a fundamental task in '''machine learning''' and '''data science''' where the goal is to predict discrete labels (categories) for given input data. It is a type of '''supervised learning''' since the model learns from labeled examples. == What is Classification? == In classification, a model is trained on a dataset with input features and known target classes. Once trained, the model can assign class labels to new, unseen dat..."

2025-06-10T05:29:21Z

Created page with "= Classification = '''Classification''' is a fundamental task in '''machine learning''' and '''data science''' where the goal is to predict discrete labels (categories) for given input data. It is a type of '''supervised learning''' since the model learns from labeled examples. == What is Classification? == In classification, a model is trained on a dataset with input features and known target classes. Once trained, the model can assign class labels to new, unseen dat..."

New page

= Classification =

'''Classification''' is a fundamental task in '''machine learning''' and '''data science''' where the goal is to predict discrete labels (categories) for given input data. It is a type of '''supervised learning''' since the model learns from labeled examples.

== What is Classification? ==

In classification, a model is trained on a dataset with input features and known target classes. Once trained, the model can assign class labels to new, unseen data points.

Examples of classification problems include:

* Email spam detection (spam or not spam)
* Disease diagnosis (disease type)
* Handwritten digit recognition (digits 0–9)
* Sentiment analysis (positive, negative, neutral)

== Types of Classification ==

Classification tasks can be divided into:

* '''Binary Classification'''
Only two classes are possible (e.g., email is spam or not spam).

* '''Multi-class Classification'''
More than two classes, each input belongs to exactly one class (e.g., digit recognition).

* '''Multi-label Classification'''
Each input can belong to multiple classes simultaneously (e.g., tagging multiple objects in an image).

== How Classification Works ==

1. '''Data Collection''' – Gather labeled data.
2. '''Feature Extraction''' – Select relevant features from raw data.
3. '''Model Training''' – Use algorithms like Logistic Regression, Decision Trees, Support Vector Machines (SVM), or Neural Networks.
4. '''Evaluation''' – Assess model using metrics such as [[Accuracy]], [[Precision]], [[Recall]], [[F1 Score]], [[Confusion Matrix]].
5. '''Prediction''' – Classify new data based on learned patterns.

== Common Classification Algorithms ==

* '''Logistic Regression''' – Estimates the probability of a binary outcome.
* '''Decision Trees''' – Model decisions with tree-like structures.
* '''Random Forest''' – Ensemble of decision trees to improve accuracy.
* '''Support Vector Machine (SVM)''' – Finds the best separating hyperplane.
* '''K-Nearest Neighbors (KNN)''' – Classifies based on closest training examples.
* '''Neural Networks''' – Mimics human brain structures for complex patterns.

== Challenges in Classification ==

* '''Imbalanced Classes''' – Some classes have very few samples.
* '''Overfitting''' – Model fits training data too well but fails on new data.
* '''Feature Selection''' – Choosing the right attributes is crucial.
* '''Noisy Data''' – Errors or outliers can confuse the model.

== Real-World Applications ==

* Medical diagnosis
* Fraud detection
* Customer churn prediction
* Image and speech recognition
* Natural language processing

== Related Pages ==

* [[Supervised Learning]]
* [[Regression]]
* [[Clustering]]
* [[Evaluation Metrics]]
* [[Confusion Matrix]]
* [[Precision]]
* [[Recall]]
* [[F1 Score]]
* [[Overfitting]]
* [[Underfitting]]

== SEO Keywords ==

classification in machine learning, types of classification, binary classification, multi-class classification, supervised learning classification, classification algorithms, classification examples, machine learning tasks

Classification - Revision history

Thakshashila: /* SEO Keywords */