Decision rule what is

If its Condition is satisfied i. Learn more in: Expression and Processing of Inductive Queries. Automatically generated standards that indicate the relationship between multimedia features and content information. Learn more in: Multimedia Representation. If the condition of the rule is satisfied, then the example belongs to the class given in the conclusion. Learn more in: Machine Learning. Find more terms and definitions using our Dictionary Search.

Decision Rule appears in:. Recommend to a Librarian Recommend to a Colleague. It looks like you're using Internet Explorer 11 or older. This website works best with modern browsers such as the latest versions of Chrome, Firefox, Safari, and Edge. If you continue with this browser, you may see unexpected results.

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The following recipe tells us how to draw an initial decision list:. The next step is to generate many new lists starting from this initial sample to obtain many samples from the posterior distribution of decision lists. The new decision lists are sampled by starting from the initial list and then randomly either moving a rule to a different position in the list or adding a rule to the current decision list from the pre-mined conditions or removing a rule from the decision list.

Which of the rules is switched, added or deleted is chosen at random. At each step, the algorithm evaluates the posteriori probability of the decision list mixture of accuracy and shortness. The Metropolis Hastings algorithm ensures that we sample decision lists that have a high posterior probability.

This procedure provides us with many samples from the distribution of decision lists. The BRL algorithm selects the decision list of the samples with the highest posterior probability. We use the SBRL algorithm to predict the risk for cervical cancer.

I first had to discretize all input features for the SBRL algorithm to work. For this purpose I binned the continuous features based on the frequency of the values by quantiles. Note that we get sensible rules, since the prediction on the THEN-part is not the class outcome, but the predicted probability for cancer.

The conditions were selected from patterns that were pre-mined with the FP-Growth algorithm. The following table displays the pool of conditions the SBRL algorithm could choose from for building a decision list. The maximum number of feature values in a condition I allowed as a user was two. Here is a sample of ten patterns:.

Next, we apply the SBRL algorithm to the bike rental prediction task. This only works if the regression problem of predicting bike counts is converted into a binary classification task. I have arbitrarily created a classification task by creating a label that is 1 if the number of bikes exceeds bikes on a day, else 0. Let us predict the probability that the number of bikes will exceed for a day in with a temperature of 17 degrees Celsius.

The first rule does not apply, since it only applies for days in The second rule applies, because the day is in and 17 degrees lies in the interval [7. They are probably the most interpretable of the interpretable models. This statement only applies if the number of rules is small, the conditions of the rules are short maximum 3 I would say and if the rules are organized in a decision list or a non-overlapping decision set.

Decision rules can be as expressive as decision trees, while being more compact. Decision trees often also suffer from replicated sub-trees, that is, when the splits in a left and a right child node have the same structure. The prediction with IF-THEN rules is fast , since only a few binary statements need to be checked to determine which rules apply.

Decision rules are robust against monotonic transformations of the input features, because only the threshold in the conditions changes. They are also robust against outliers, since it only matters if a condition applies or not. They select only the relevant features for the model.

For example, a linear model assigns a weight to every input feature by default. Simple rules like from OneR can be used as baseline for more complex algorithms. While you can always divide a continuous target into intervals and turn it into a classification problem, you always lose information.

In general, approaches are more attractive if they can be used for both regression and classification. Often the features also have to be categorical. That means numeric features must be categorized if you want to use them. There are many ways to cut a continuous feature into intervals, but this is not trivial and comes with many questions without clear answers. How many intervals should the feature be divided into?

What is the splitting criteria: Fixed interval lengths, quantiles or something else? Categorizing continuous features is a non-trivial issue that is often neglected and people just use the next best method like I did in the examples. Many of the older rule-learning algorithms are prone to overfitting. The algorithms presented here all have at least some safeguards to prevent overfitting: OneR is limited because it can only use one feature only problematic if the feature has too many levels or if there are many features, which equates to the multiple testing problem , RIPPER does pruning and Bayesian Rule Lists impose a prior distribution on the decision lists.

Decision rules are bad in describing linear relationships between features and output. That is a problem they share with the decision trees. Decision trees and rules can only produce step-like prediction functions, where changes in the prediction are always discrete steps and never smooth curves.

This is related to the issue that the inputs have to be categorical. In decision trees, they are implicitly categorized by splitting them. OneR is implemented in the R package OneR , which was used for the examples in this book. OneR is also implemented in the Weka machine learning library and as such available in Java, R and Python.

Additionally, I recommend the imodels package , which implements rule-based models such as Bayesian rule lists, CORELS, OneR, greedy rule lists, and more in a Python package with a unified scikit-learn interface. I will not even try to list all alternatives for learning decision rule sets and lists, but will point to some summarizing work.

It is an extensive work on learning rules, for those who want to delve deeper into the topic. It provides a holistic framework for thinking about learning rules and presents many rule learning algorithms. Holte, Robert C. Cohen, William W. Letham, Benjamin, et al. Borgelt, C. Interpretable machine learning Summary 1 Preface by the Author 2 Introduction 2. Interpretable Machine Learning. Lower-Tailed Test a Z 0.

Compute the test statistic. Step 5. Things to Remember When Interpreting P Values P-values summarize statistical significance and do not address clinical significance. There are instances where results are both clinically and statistically significant - and others where they are one or the other but not both.

This is because P-values depend upon both the magnitude of association and the precision of the estimate the sample size. When the sample size is large, results can reach statistical significance i. Conversely, with small sample sizes, results can fail to reach statistical significance yet the effect is large and potentially clinical important.

It is extremely important to assess both statistical and clinical significance of results. Statistical tests allow us to draw conclusions of significance or not based on a comparison of the p-value to our selected level of significance. When conducting any statistical analysis, there is always a possibility of an incorrect conclusion.

With many statistical analyses, this possibility is increased. Investigators should only conduct the statistical analyses e. Many investigators inappropriately believe that the p-value represents the probability that the null hypothesis is true. P-values are computed based on the assumption that the null hypothesis is true. The p-value is the probability that the data could deviate from the null hypothesis as much as they did or more. Consequently, the p-value measures the compatibility of the data with the null hypothesis, not the probability that the null hypothesis is correct.

Statistical significance does not take into account the possibility of bias or confounding - these issues must always be investigated. Evidence-based decision making is important in public health and in medicine, but decisions are rarely made based on the finding of a single study. Replication is always important to build a body of evidence to support findings. Step 4. The most common reason for a Type II error is a small sample size. Upper-tailed, Lower-tailed, Two-tailed Tests The research or alternative hypothesis can take one of three forms.

Upper-Tailed Test. Lower-Tailed Test.