At the end of this phase, a decision on using the data mining results should be reached.
@avikumart_
-
Evaluating AI Models on Unseen Data and Performance Metrics
By
–
Models might be performing well on your training data, but it is necessary to test and evaluate it on unseen data for the model to achieve its objectives. Appropriate evaluation metrics are measured and well-tested at this stage.
-
Evaluation Phase in Data Science Project Lifecycle
By
–
5. Evaluation The evaluation phase is one of the most crucial phases of any data science project lifecycle. At this stage in your project, you must have built machine-learning models.
-
Data Preparation Phase Requirements for AI Techniques
By
–
Some techniques have specific requirements for the format of data it needs. Therefore, going back to the data preparation phase is often necessary at this stage.
-
Modeling Techniques and Parameter Calibration in Data Mining
By
–
4. Modeling In this phase, various modeling techniques depending on the problem statement, are selected and applied, and their parameters are calibrated to find optimal modeling performance. Typically, there are several techniques for the same data mining problem type.
-
Data Preparation Techniques for Machine Learning Models
By
–
Tasks include data table, feature selection, feature engineering, as well as feature transformation and cleaning of data for modeling tools and techniques.
-
Data Preparation Phase for AI Modeling
By
–
3. Data Preparation The data preparation phase covers all activities needed to construct the final dataset fed into the modeling tools from the initial raw data. Data preparation tasks are likely to be performed multiple times and not in any prescribed order.
-
Data Understanding Phase: Initial Collection and Exploratory Analysis
By
–
2. Data Understanding The data understanding phase starts with initial data collection and proceeds with activities such as feature description, primary data analysis, and exploratory data analysis that enable…
-
Data Quality Issues and Hypothesis Formation in AI Datasets
By
–
…you to become familiar with the data, identify the data quality problems such as missing values, inconsistent data entries, and/or identify compelling subsets to form a hypothesis regarding confidential information.
-
Business Problem Understanding Phase Data Mining
By
–
1. Business problem understanding This initial phase focuses on understanding the project goals, objectives, and requirements from a business perspective, then converting this knowledge into a data mining problem definition and a preliminary plan to archive desired outcomes.