Table of Contents
- 1 What is descriptive and predictive task?
- 2 What are descriptive tasks?
- 3 What is descriptive task in machine learning?
- 4 What is descriptive learning in machine learning?
- 5 What is descriptive rule learning in machine learning?
- 6 What is descriptive and predictive analysis?
- 7 What is predictive data mining?
What is descriptive and predictive task?
Descriptive mining tasks describe the characteristics of the data in a target data set. On the other hand, predictive mining tasks carry out the induction over the current and past data so that predictions can be made.
What is predictive task in machine learning?
In this article A machine learning task is the type of prediction or inference being made, based on the problem or question that is being asked, and the available data. For example, the classification task assigns data to categories, and the clustering task groups data according to similarity.
What are descriptive tasks?
Descriptive tasks require you to display facts, figures, or knowledge. Descriptive problems, sometimes knows as simple problems, are those that require you to use facts, figures, or knowledge in a simple way. As Brick suggests, they ask you to apply basic concepts and straightforward tools.
What are predictive tasks?
Prediction task predicts the possible values of missing or future data. Prediction involves developing a model based on the available data and this model is used in predicting future values of a new data set of interest.
What is descriptive task in machine learning?
The descriptive analysis uses mainly unsupervised learning approaches for summarizing, classifying, extracting rules to answer what happens was happened in the past. While Predictive analysis is about machine learning approaches for the aim forecasting future data based on past data.
What is descriptive machine learning?
Descriptive analysis is used to understand the past and predictive analysis is used to predict the future. Both of these concepts are important in machine learning because a clear understanding of the problem and its implications is the best way to make the right decisions.
What is descriptive learning in machine learning?
What is meant by descriptive learning?
Descriptive learning theories make statements about how learning occurs and devise models that can be used to explain and predict learning results. They are often based on descriptive theories; sometimes they are derived from experience. Instructional design is the umbrella which assembles prescriptive theories.
What is descriptive rule learning in machine learning?
Supervised descriptive rule induction (SDRI) is a machine learning task in which individual patterns in the form of rules (see Classification rule) intended for interpretation are induced from data, labeled by a predefined property of interest.
What are descriptive and predictive analytics in machine learning?
Let’s take a look at descriptive and predictive analytics in machine learning one by one. Before using a machine learning algorithm, it is very important to acquire abstract knowledge of the problem. The goal of descriptive analysis is to find an accurate understanding of the problem by asking questions from historical data.
What is descriptive and predictive analysis?
Descriptive and Predictive Analysis are types of statistical analysis techniques structured as a sequence of steps that you need to take to gain comprehensive domain knowledge to solve complex business problems. These techniques give you a clear understanding of the business problem so that you can make the right decisions.
Can machine learning be used to train hidden data?
Once our machine learning model is trained and tested for a relatively smaller dataset, then the same method can be applied to hidden data. The data effectively need not be biased as it would result in bad decision making. In the case of predictive analysis, data is useful when it is complete, accurate and substantial.
What is predictive data mining?
Predictive Data Mining: The main goal of this mining is to say something about future results not of current behaviour. It uses the supervised learning functions which are used to predict the target value. The methods come under this type of mining category are called classification, time-series analysis and regression.