Support

1. Accuracy

The accuracy of a data mining model is a measure of how correctly the model predicts the target values. The accuracy is measured on a test dataset, which is separate from the training dataset that was used to train the model. There are a number of ways to measure accuracy, but the most common is to calculate the percentage of correct predictions. This is known as the accuracy rate....

2. Classification Accuracy

This measures how accurately the patterns discovered by the algorithm can be used to classify new data. This is typically done by taking a set of data that has been labeled with known class labels and then using the discovered patterns to predict the class labels of the data. The accuracy can then be computed by comparing the predicted labels to the actual labels....

3. Clustering Accuracy

This measures how accurately the patterns discovered by the algorithm can be used to cluster new data. This is typically done by taking a set of data that has been labeled with known cluster labels and then using the discovered patterns to predict the cluster labels of the data. The accuracy can then be computed by comparing the predicted labels to the actual labels....

4. Coverage

This measures how many of the possible patterns in the data are discovered by the algorithm. This can be computed by taking the total number of possible patterns and dividing it by the number of patterns discovered by the algorithm. A Coverage Pattern is a type of sequential pattern that is found by looking for items that tend to appear together in sequential order. For example, a coverage pattern might be “customers who purchase item A also tend to purchase item B within the next month.”...

5. Visual Inspection

This is perhaps the most common method, where the data miner simply looks at the patterns to see if they make sense. In visual inspection, the data is plotted in a graphical format and the pattern is observed. This method is used when the data is not too large and can be easily plotted. It is also used when the data is categorical in nature. Visual inspection is a pattern evaluation method in data mining where the data is visually inspected for patterns. This can be done by looking at a graph or plot of the data, or by looking at the raw data itself. This method is often used to find outliers or unusual patterns....

6. Running Time

This measures how long it takes for the algorithm to find the patterns in the data. This is typically measured in seconds or minutes. There are a few different ways to measure the performance of a machine learning algorithm, but one of the most common is to simply measure the amount of time it takes to train the model and make predictions. This is known as the running time pattern evaluation....

7. Support

The support of a pattern is the percentage of the total number of records that contain the pattern. Support Pattern evaluation is a process of finding interesting and potentially useful patterns in data. The purpose of support pattern evaluation is to identify interesting patterns that may be useful for decision-making. Support pattern evaluation is typically used in data mining and machine learning applications....

8. Confidence

The confidence of a pattern is the percentage of times that the pattern is found to be correct. Confidence Pattern evaluation is a method of data mining that is used to assess the quality of patterns found in data. This evaluation is typically performed by calculating the percentage of times a pattern is found in a data set and comparing this percentage to the percentage of times the pattern is expected to be found based on the overall distribution of data. If the percentage of times a pattern is found is significantly higher than the expected percentage, then the pattern is said to be a strong confidence pattern....

9. Lift

The lift of a pattern is the ratio of the number of times that the pattern is found to be correct to the number of times that the pattern is expected to be correct. Lift Pattern evaluation is a data mining technique that can be used to evaluate the performance of a predictive model. The lift pattern is a graphical representation of the model’s performance and can be used to identify potential problems with the model....

10. Prediction

The prediction of a pattern is the percentage of times that the pattern is found to be correct. Prediction Pattern evaluation is a data mining technique used to assess the accuracy of predictive models. It is used to determine how well a model can predict future outcomes based on past data. Prediction Pattern evaluation can be used to compare different models, or to evaluate the performance of a single model....

11. Precision

Precision Pattern Evaluation is a method for analyzing data that has been collected from a variety of sources. This method can be used to identify patterns and trends in the data, and to evaluate the accuracy of data. Precision Pattern Evaluation can be used to identify errors in the data, and to determine the cause of the errors. This method can also be used to determine the impact of the errors on the overall accuracy of the data....

12. Cross-Validation

This method involves partitioning the data into two sets, training the model on one set, and then testing it on the other. This can be done multiple times, with different partitions, to get a more reliable estimate of the model’s performance. Cross-validation is a model validation technique for assessing how the results of a data mining analysis will generalize to an independent data set. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice. Cross-validation is also referred to as out-of-sample testing....

13. Test Set

This method involves partitioning the data into two sets, training the model on the entire data set, and then testing it on the held-out test set. This is more reliable than cross-validation but can be more expensive if the data set is large. There are a number of ways to evaluate the performance of a model on a test set. The most common is to simply compare the predicted labels to the true labels and compute the percentage of instances that are correctly classified. This is called accuracy. Another popular metric is precision, which is the number of true positives divided by the sum of true positives and false positives. The recall is the number of true positives divided by the sum of true positives and false negatives. These metrics can be combined into the F1 score, which is the harmonic mean of precision and recall....

14. Bootstrapping

This method involves randomly sampling the data with replacement, training the model on the sampled data, and then testing it on the original data. This can be used to get a distribution of the model’s performance, which can be useful for understanding how robust the model is. Bootstrapping is a resampling technique used to estimate the accuracy of a model. It involves randomly selecting a sample of data from the original dataset and then training the model on this sample. The model is then tested on another sample of data that is not used in training. This process is repeated a number of times, and the average accuracy of the model is calculated....