Classification And Prediction-Model Construction And Model Usage
Classification and Prediction-
Classification-
Classification is means that the class name and object name are known in classification model. The lots of data are available in database and with use of classification predict future values for giving a better decisions. Classification data mining data giving from transnational database with different attributes.
Prediction-
prediction means giving a current data with descriptive manner and predict a future outcomes for or that data.
Process and Model Construction of Classification-
Model Construction-
The model construction defines the all predefined classes that are related with each other and all training set data from large data. The sample data are collected from that training set data. The model represented with classification rule. Decision tree for mathematical formations for better output.
In model construction the all training data is available and that all data is giving to classification algorithm that algorithm are giving to classifier model and classified model defines a classified data well in manner.
Model Usage-
In model usage that are used for classifying future outcomes with unknown object. In that method known label compare with unknown data and predict a final result. Accuracy rate is checked with over fitting condition.The model usage is defined as testing data are giving to classifier. That classified are tested data with accuracy of that data and they confirm the classification is done with accuracy.
Explanation :
Classification and prediction are two fundamental techniques in data mining used to extract useful knowledge and make decisions from large datasets. Classification deals with predicting categorical class labels, while prediction is concerned with forecasting continuous numerical values. Both processes involve two main steps: model construction and model usage.
The model construction phase focuses on building a model that can accurately represent the relationship between input attributes and target outputs. This step uses a training dataset, which contains known class labels or values. The algorithm analyzes the patterns and correlations in this data to form classification or prediction rules. For classification, techniques such as Decision Trees, Naive Bayes, Neural Networks, and Support Vector Machines (SVM) are commonly used. For prediction, algorithms like Linear Regression, Regression Trees, and Neural Networks are often applied. During construction, the model is also validated using techniques such as cross-validation or holdout testing to ensure accuracy and reliability.
Once the model is built, the next step is model usage, where the constructed model is applied to new, unseen data to predict outcomes. In classification, the model assigns class labels to new data instances—for example, predicting whether a customer will default on a loan. In prediction, the model estimates continuous values, such as sales growth or housing prices. The effectiveness of model usage depends on how well the model generalizes to new data rather than just fitting the training data.
To ensure better performance, models are often evaluated and refined using metrics such as accuracy, precision, recall, F-measure, and mean squared error. Misclassified records or prediction errors help identify areas where the model needs improvement. Additionally, visualization tools and confusion matrices assist in understanding the model’s decision-making process.
In summary, model construction builds the foundation by learning from historical data, while model usage applies that knowledge to real-world problems. Together, they enable organizations to make data-driven predictions and decisions, improving planning, efficiency, and strategic outcomes in domains such as marketing, healthcare, finance, and business intelligence.
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