Classification Techniques

Table of Contents

Classification is a predictive modeling technique where the outcome variable is categorical. Classification aims to predict the class label of new instances based on past observations. Here’s a detailed explanation of the basics of classification:

Students Dataset

Student Dataset
download Dataset

  • Key Concepts:

    1. Class Labels:
      • Discrete or Nominal: These are the categories or classes the data points belong to. For example, in a dataset of students, the class labels could be their chosen bachelor types, such as Computer Science, Business Administration, Nursing, Medical Field, and Engineering.

        Consider a dataset of students with attributes like Student_Gender, Bachelor_Type, Financial_Aid, GPA, and Graduates. Here’s how discrete numbers and nominal dimensions apply:

        1. Bachelor_Type:

          • Categories: Computer Science, Business Administration, Nursing, Medical Field, Engineering.
          • Discrete Numbers (Encoding): 0, 1, 2, 3, 4.
          • Nominal Dimension: 5.

        2. Graduates:

          • Categories: Yes, No.
          • Discrete Numbers (Encoding): 0 for No, 1 for Yes.
          • Nominal Dimension: 2.

        3. GPA:

          • Categories: <=2.0, 2.0-2.8, 3.0-3.5, 3.5-4.0.
          • Discrete Numbers (Encoding): 0, 1, 2, 3.
          • Nominal Dimension: 4.
      • Training Set:
        • The set of data used to build the classification model. It contains the observations and their known class labels.
        • Test Set:
          • A separate set of data was used to evaluate the classification model’s accuracy. The test set is independent of the training set to ensure the model’s generalization capability.

          • Training Data: A dataset with known labels (categories) is used to train a model. Each instance in the training data consists of features (input variables) and a corresponding label (output category).

          • Model Training: A machine learning algorithm is applied to the training data to learn the relationships between the features and the labels. The model’s parameters are adjusted to minimize the error in predicting the labels.

          • Prediction: Once trained, the model can be used to predict the labels for new, unseen data. The model uses the learned relationships to assign a class label to each new instance.

          • Evaluation: The performance of the classification model is evaluated using metrics such as accuracy, precision, recall, and F1-score, often by comparing the predicted labels to the true labels on a test dataset. The process typically involves

  1.  

Common Algorithms for Classification

  • Logistic Regression: A statistical method for binary classification.
  • Decision Trees: A tree-like model of decisions and their possible consequences.
  • Random Forest: An ensemble of decision trees, improving accuracy by averaging multiple trees.
  • Support Vector Machines (SVM): Finds the hyperplane that best separates the classes in the feature space.
  • Naive Bayes: A probabilistic classifier based on Bayes’ theorem, assuming independence between features.
  • K-Nearest Neighbors (KNN): Classifies based on the majority class among the k-nearest neighbors in the feature space.
  • Neural Networks: Complex models that can capture non-linear relationships in the data.

Applications of Classification

  • Spam Detection: Classifying emails as spam or not spam.
  • Medical Diagnosis: Predicting the presence or absence of a disease.
  • Image Recognition: Identifying objects or people in images.
  • Sentiment Analysis: Determining the sentiment (positive, negative, neutral) in text data.
  • Credit Scoring: Assessing the risk of lending to a borrower.

Logistic regression is a statistical method used for binary classification tasks, where the outcome variable is categorical and typically binary (e.g., yes/no, success/failure, true/false). It models the probability that a given input point belongs to a certain class. Here’s a detailed explanation:

Key Concepts of Logistic Regression

  1. Binary Outcome Variable:

    • Logistic regression predicts the probability of the outcome variable being one of the two possible categories.

  2. Logistic Function (Sigmoid Function):

    • The logistic regression model uses the logistic function to transform the linear combination of input features into a probability value between 0 and 1.
    • The logistic function is given by:
Logistics Function

Logistic Regression

Possible questions and Answers Using Logistic Regression.

Question: What is the probability that a student will graduate based on their GPA?
Question: How does receiving financial aid impact the likelihood of graduating?
Question: Does gender influence graduation rates?
Question: What is the impact of the type of bachelor’s degree on graduation likelihood?

Using WEKA for Data Mining Analysis.

Download WEKA https://waikato.github.io/weka-wiki/downloading_weka/ 

Weka uses Java, so make sure that you have Java installed. You can check your CMD terminal in Windows and type java—-version. Otherwise, just download Java and install it. https://www.java.com/en/

 

Classification - Logistic Regression with Student Dataset

Decision Tree Induction

Dataset

Decision tree induction is the process of creating a decision tree from a set of training data with known class labels. A decision tree is a flowchart-like structure where:

  • Each internal node represents a test on an attribute.
  • Each branch represents the outcome of the test.
  • Each leaf node holds a class label.

The topmost node is called the root node.

Typical applications
Credit/loan approval:
Medical diagnosis: if a tumor is cancerous or benign
Fraud detection: if a transaction is fraudulent
Web page categorization: which category it is.

  • Spam Detection: Classifying emails as spam or not spam.
  • Medical Diagnosis: Predicting the presence or absence of a disease.
  • Image Recognition: Identifying objects or people in images.
  • Sentiment Analysis: Determining the sentiment (positive, negative, neutral) in text data.
  • Credit Scoring: Assessing the risk of lending to a borrower.

Introduction to Decision Trees

A decision tree is a popular and powerful classification and prediction tool used in data mining and machine learning. It helps in making decisions by following a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.

Key Concepts

  1. Node Types:

    • Root Node: Represents the entire dataset, which is split into two or more homogeneous sets.
    • Decision Nodes: Nodes where the dataset is split based on an attribute.
    • Leaf Nodes (Terminal Nodes): Nodes that predict the outcome, often labeled with the class of the data points that fall into that category.

  2. Branches: Represent decision rules or conditions to partition the data.

Steps in Decision Tree 

  1. Model Construction:

    • Selection of Attribute: At each node, the best attribute is chosen using a selection measure like Information Gain or Gini Index.
    • Splitting: The dataset is split based on the selected attribute. This process is recursive and continues until stopping criteria are met (e.g., all samples belong to the same class or no further attributes are left).

  2. Model Usage:

    • Once constructed, the decision tree can classify new data points by traversing the tree from the root to a leaf node based on the attribute values of the data points.

Decision Tree Construction Process

We will build a decision tree to predict whether a student graduates based on the given attributes.

  1. Select the Target Variable: The target variable is what we aim to predict.
  2. Select Feature Variables: The feature variables are RandomN, Student Gender, Bachelor Type, Financial Aid, and GPA.

  3. Example: Decision Tree Induction with the Student Dataset

    Let’s illustrate the decision tree construction with an example:

    1. Starting at the Root:

      • We start by evaluating each attribute to find the one that best splits the data regarding the target variable Graduates.

    2. Creating Branches:

      • Assume the attribute GPA provides the best split initially. We create branches based on GPA ranges (e.g., <=2.0, 2.0-2.8, 3.0-3.5, 3.5-4.0).

    3. Further Splitting:

      • For each GPA branch, we evaluate other attributes like Bachelor Type, Financial Aid, etc., to create further splits.

Data Mining - Concepts and Techniques - Jiawei Han, Micheline Kamber, Jian Pei

Data warehouse and OLAP technology for data mining--Data processing
Download Book Here

Data Mining - Concepts and Techniques - Jiawei Han, Micheline Kamber, Jian Pei

Data warehouse and OLAP technology for data mining--Data processing

Download Book Here
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