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dm:start:guidelines
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dm:start:guidelines [14/11/2016 alle 14:13 (7 anni fa)] Anna Monreale [Guidelines for the task on data understanding] |
dm:start:guidelines [23/11/2020 alle 10:34 (3 anni fa)] (versione attuale) Riccardo Guidotti [Guidelines for the task on Classification] |
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| ====== Guidelines for the task on Data Understanding ====== | ====== Guidelines for the task on Data Understanding ====== | ||
| * Data understanding (30 points) | * Data understanding (30 points) | ||
| - | | + | |
| - | * Distribution of the variables and statistics (7 points) | + | - Distribution of the variables and statistics (7 points) |
| - | * Assessing data quality (missing values, outliers) (7 points) | + | - Assessing data quality (missing values, outliers) (7 points) |
| - | * Variables transformations (6 points) | + | - Variables transformations (6 points) |
| - | * Pairwise correlations and eventual elimination of redundant variables (7 points) | + | - Pairwise correlations and eventual elimination of redundant variables (7 points) |
| Linea 26: | Linea 26: | ||
| ====== Guidelines for the task on Association Rules Mining ====== | ====== Guidelines for the task on Association Rules Mining ====== | ||
| - | * Frequent patterns extraction with different values of support and different types (i.e. frequent, close maximal), (5 points) | + | * Frequent patterns extraction with different values of support and different types (i.e. frequent, close, maximal), (6 points) |
| - | * Discussion of the most interesting frequent patterns (6 points) | + | * Discussion of the most interesting frequent patterns |
| - | * Association rules extraction with different values of confidence (5 points) | + | * Association rules extraction with different values of confidence (6 points) |
| - | * Discussion of the most interesting rules (6 points) | + | * Discussion of the most interesting rules and analyze how changes the number of rules w.r.t. the min_conf parameter, histogram of rules' confidence and lift (7 points) |
| - | * Use the most meaningful rules to replace missing values and evaluate the accuracy | + | * Use the most meaningful rules to replace missing values and evaluate the accuracy (2 points) |
| - | * Use the most meaningful rules to predict | + | * Use the most meaningful rules to predict |
| ====== Guidelines for the task on Classification ====== | ====== Guidelines for the task on Classification ====== | ||
| - | * Learning of different decision trees with different parameters and gain formulas with the object of maximizing the performances (12 points) | + | * Learning of different decision trees/ |
| - | * Decision trees interpretation (6 points) | + | * Decision trees interpretation, validation with test and training set (6 points) |
| - | | + | |
| * Discussion of the best prediction model (6 points) | * Discussion of the best prediction model (6 points) | ||
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| * Only PDF file are allowed, you do not have to submit python code or the knime workflows. | * Only PDF file are allowed, you do not have to submit python code or the knime workflows. | ||
| * The final paper must be easily readable, i.e., it is better to use font size higher than 9pt. | * The final paper must be easily readable, i.e., it is better to use font size higher than 9pt. | ||
| - | * Use a readable font size, e.g. Arial, Times New Romans | + | * Use a readable font type and size, e.g. Arial, Times New Romans |
| * You can use multiple columns and change the margin size but the project must be readable. | * You can use multiple columns and change the margin size but the project must be readable. | ||
| * It is NOT required to put python code, knime flows, or theoretical descriptions of the algorithm in the final paper. | * It is NOT required to put python code, knime flows, or theoretical descriptions of the algorithm in the final paper. | ||
dm/start/guidelines.1479132781.txt.gz · Ultima modifica: 14/11/2016 alle 14:13 (7 anni fa) da Anna Monreale
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