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classification in data science

Classification in Data Science

Module - 3 Supervised Learning
Classification in Data Science

Overview

Classification is a supervised learning technique in machine learning that assigns a class label to input data points. It is used to predict the class of data points given a set of features. Classification algorithms determine which class the data point belongs to by learning from the training data and then making predictions on unseen data. The most common types of classification algorithms are k-nearest neighbours, decision trees, logistic regression, naive Bayes, and support vector machines.

What is classification?

Within the context of the healthcare industry, classification could be a vital errand utilized for different purposes, such as diagnosis, treatment, investigate, and charging.

One case of classification in healthcare is the International Classification of Diseases (ICD) framework. The ICD could be a standardized framework utilized by healthcare suppliers to classify and code illnesses, wounds, and other health-related conditions. This framework empowers healthcare suppliers to communicate and share data approximately patients' conditions and medicines over diverse nations and healthcare settings.

For case, in case a understanding includes a therapeutic condition such as diabetes, their healthcare supplier will dole out a code from the ICD framework to show the sort and seriousness of the infection. This code can at that point be utilized for different purposes, such as following the predominance of diabetes in a populace, observing the patient's wellbeing status, and charging protections suppliers for the patient's treatment.

Types of classification

  1. Hierarchical classification may be a form of classification in which objects and entities are sorted into categories based on their characteristics and connections. Hierarchical classification divides objects into smaller and smaller sub-categories as you move down the hierarchy. For case, in a hierarchical classification of creatures, the beat level may be separated into warm blooded animals, reptiles, angle, and feathered creatures. Beneath each of those categories, you'll discover encourage divisions like rodents, creatures of land and water, and so on.
  2. Non-hierarchical classification could be a form of classification in which objects and entities are sorted into categories based on their characteristics but without any various leveled structure. For example, in a non-hierarchical classification of creatures, all creatures may be separated into four isolated categories: warm blooded creatures, reptiles, fish, and fowls.
  3. Binary classification may be a type of classification in which an object or entity is classified into one of two particular categories. For case, a double classification of creatures can be “mammal” or “non-mammal”.
  4. Multi-class classification could be a sort of classification in which an object or entity is classified into more than two particular categories. For illustration, a multi-class classification of creatures can be “mammal”, “reptile”, “fish”, and “bird”.

Challenges and limitations of classification

  • Class imbalance: Class imbalance occurs when the number of samples belonging to one class is much greater than the number of samples belonging to the other classes. This can make it difficult for a classifier to accurately classify data points because it will tend to heavily weight the more prevalent class.
  • Overfitting: Overfitting occurs when a model is trained too much on a specific set of data, causing it to become too specialized to generalize well on unseen data. If a model is overfitted, it will perform well on the training data but will not be able to generalize well to new data.
  • Curse of dimensionality: The curse of dimensionality refers to the difficulty of accurately classifying high-dimensional data. As the number of dimensions in a dataset increases, the amount of data needed to accurately classify it also increases exponentially. This makes it difficult to accurately classify high-dimensional datasets with limited data.

Real-world examples of classification

  1. Spam Filtering: Spam filtering is a form of classification that is used to automatically detect and filter out unwanted emails and other forms of digital communication. A spam filter uses a set of rules and algorithms to identify emails that are likely to be spam and then moves them to a separate folder or deletes them entirely.
  2. Disease Diagnosis: Disease diagnosis is a form of classification that is used to identify a particular type of illness or condition. It involves collecting data about the patient’s symptoms, medical history, and other factors in order to make a diagnosis.
  3. Fraud Detection: Fraud detection is a form of classification that is used to identify fraudulent activity such as credit card fraud or identity theft. It involves using data points such as purchase history, location, and account activity to determine if a transaction is suspicious or not.

Algorithms in Classification

  1. Logistic Regression: This is a classification algorithm used to predict a binary outcome (e.g. yes/no, 0/1, true/false) based on independent variables. It uses an equation to determine the probability of an event occurring, and then uses a threshold value to determine the outcome.
  2. K-Nearest Neighbors (KNN): This is a non-parametric, supervised machine learning algorithm used for classification. It works by finding the K (usually 3-5) nearest points in the dataset, and then assigning a class label based on the majority class among them.
  3. Support Vector Machines (SVM): This is a supervised machine learning algorithm used for classification and regression. It works by finding a hyperplane that separates the data points into their respective classes.
  4. Decision Tree: This is a supervised machine learning algorithm used for both classification and regression. It works by constructing a decision tree from the training data, which is then used to make predictions on unseen data points.
  5. Naive Bayes: This is a supervised machine learning algorithm used for classification. It works by using the Bayes theorem to calculate the probability of an event occurring, given a set of evidence.
  6. Random Forest: This is an ensemble machine-learning algorithm used for both classification and regression. It works by randomly selecting a subset of features, and then building multiple decision trees from the dataset.
  7. Neural Networks: This is a supervised machine learning algorithm used for both classification and regression. It works by creating a network of neurons, which are connected together and used to make predictions.
  8. Gradient Boosting Machines (GBM): This is an ensemble machine learning algorithm used for both classification and regression. It works by constructing a series of decision trees and then combining them together to make predictions.
  9. AdaBoost: AdaBoost is an ensemble machine-learning algorithm used for both classification and regression. It works by constructing multiple weak learners, and then combining them together to make predictions.

Conclusion

After the company used classification in ML, they were able to accurately classify their customers into different segmentations. This enabled them to target their marketing campaigns to the most likely customers, which resulted in higher conversion rates and increased profits for the business.

Key takeaways

  1. Understand the basics of building a classification model, including supervised vs unsupervised learning, feature engineering, and model selection.
  2. Choose an appropriate performance metric for your model, such as accuracy, precision, recall, or F1 score.
  3. Create a training and testing dataset with appropriate labels.
  4. Pre-process the data to increase accuracy and reduce bias.
  5. Select a classification algorithm, such as logistic regression, naive Bayes, support vector machines, decision trees, or random forests.
  6. Train your model using the training dataset and evaluate it using the testing dataset.
  7. Tune the hyperparameters to improve the model's performance.
  8. Monitor the model's performance over time and adjust it as needed.

Quiz

  1. What type of problem is classification? 
    1. Supervised Learning 
    2. Unsupervised Learning 
    3. Reinforcement Learning 
    4. Both A and C

Answer: A. Supervised Learning

  1. What is the key difference between a classification and regression problem? 
    1. Classification predicts discrete values while regression predicts continuous values. 
    2. Classification predicts continuous values while regression predicts discrete values. 
    3. Classification is supervised while regression is unsupervised. 
    4. Classification is unsupervised while regression is supervised.

Answer: a. Classification predicts discrete values while regression predicts continuous values.

  1. What is logistic regression used for in classification?  
    1. To classify data points into discrete classes. 
    2. To identify the most important features for a given classification problem.  
    3. To predict the probability of a given data point belonging to a particular class.
    4. To identify relationships between different classes.

Answer: c. To predict the probability of a given data point belonging to a particular class.

  1. What type of algorithm is k-Nearest Neighbor (k-NN)?  
    1. Supervised Learning 
    2. Unsupervised Learning 
    3. Reinforcement Learning 
    4. Both A and B

Answer: D. Both A and B

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