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The Microsoft Microsoft Decision Trees algorithm is a hybrid algorithm that incorporates different methods for creating a tree, and supports multiple analytic tasks, including regression, classification, and association.
The Microsoft Decision Trees algorithm supports modeling of both discrete and continuous attributes. This topic explains the implementation of the algorithm, describes how to customize the behavior of the algorithm for different tasks, and provides links to additional information about querying decision tree models. The Microsoft Decision Trees algorithm applies the Bayesian approach to learning causal interaction models by obtaining approximate posterior distributions for the models.
For a detailed explanation of this approach, see the paper on the Microsoft Research site, by Structure and Parameter Learning. The methodology for assessing the information value of the priors needed for learning is based on the assumption of likelihood equivalence.
This assumption says that data should not help to discriminate network structures that otherwise represent the same assertions of conditional independence. Each case is assumed to have a single Bayesian prior network and a single measure of confidence for that network.
Using these prior networks, the algorithm then computes the relative posterior probabilities of network structures given the current training data, and identifies the network structures that have the highest posterior probabilities. The Microsoft Decision Trees algorithm uses different methods to compute the best tree.
The method used depends on the task, which can be linear regression, classification, or association analysis. A single model can contain multiple trees for different predictable attributes. Moreover, each tree can contain multiple branches, depending on how many attributes and values there are in the data.
The shape and depth of the tree built in a particular model depends on the scoring method and other parameters that were used. Changes in the parameters can also affect where the nodes split. When the Microsoft Decision Trees algorithm creates the set of possible input values, it performs feature selection to identify the attributes and values that provide the most information, and removes from consideration the values that are very rare. The algorithm also groups values into bins , to create groupings of values that can be processed as a unit to optimize performance.
A tree is built by determining the correlations between an input and the targeted outcome. Nachdem alle Attribute mit Korrelationen versehen wurden, identifiziert der Algorithmus das einzige Attribut, das die Ergebnisse am saubersten trennt. After all the attributes have been correlated, the algorithm identifies the single attribute that most cleanly separates the outcomes.
Dieser Punkt der besten Trennung wird mit einer Gleichung gemessen, die den Informationsgewinn berechnet. This point of the best separation is measured by using an equation that calculates information gain. The attribute that has the best score for information gain is used to divide the cases into subsets, which are then recursively analyzed by the same process, until the tree cannot be split any more.
The exact equation used to evaluate information gain depends on the parameters set when you created the algorithm, the data type of the predictable column, and the data type of the input.
When the predictable attribute is discrete and the inputs are discrete, counting the outcomes per input is a matter of creating a matrix and generating scores for each cell in the matrix. Wenn hingegen das vorhersagbare Attribut diskret ist und die Eingaben kontinuierlich sind, wird die Eingabe der kontinuierlichen Spalten automatisch diskretisiert.
However, when the predictable attribute is discrete and the inputs are continuous, the input of the continuous columns are automatically discretized. You can accept the default and have Analysis Services Analysis Services find the optimum number of bins, or you can control the manner in which continuous inputs are discretized by setting the DiscretizationMethod and DiscretizationBucketCount properties.
Bei kontinuierlichen Attributen bestimmt der Algorithmus anhand einer linearen Regression, wo sich die Entscheidungsstruktur teilt. For continuous attributes, the algorithm uses linear regression to determine where a decision tree splits.
When the predictable attribute is a continuous numeric data type, feature selection is applied to the outputs as well, to reduce the possible number of outcomes and build the model faster. The Combination of Knowledge and Statistical Data. For a more detained explanation about how the Microsoft Microsoft Decision Trees algorithm works with discrete predictable columns, see Learning Bayesian Networks: For more information about how the Microsoft Microsoft Decision Trees algorithm works with a continuous predictable column, see the appendix of Autoregressive Tree Models for Time-Series Analysis.
The Microsoft Decision Trees algorithm offers three formulas for scoring information gain: Shannon's entropy, Bayesian network with K2 prior, and Bayesian network with a uniform Dirichlet distribution of priors. Alle drei Methoden sind im Data Mining-Bereich etabliert.
All three methods are well established in the data mining field. Es wird empfohlen, mit verschiedenen Parametern und Bewertungsmethoden zu experimentieren, um festzustellen, welche die besten Ergebnisse erzielen. We recommend that you experiment with different parameters and scoring methods to determine which provides the best results. For more information about these scoring methods, see Feature Selection. All Analysis Services Analysis Services data mining algorithms automatically use feature selection to improve analysis and reduce processing load.
The method used for feature selection depends on the algorithm that is used to build the model. Die Klassifizierung ist eine wichtige Data Mining-Strategie. Classification is an important data mining strategy. Generally, the amount of information that is needed to classify the cases grows in direct proportion to the number of input records.
This limits the size of the data that can be classified. The Microsoft Decision Trees algorithm using uses the following methods to resolve these problems, improve performance, and eliminate memory restrictions: Funktionsauswahl zur Optimierung der Auswahl von Attributen.
Feature selection to optimize the selection of attributes. Bayes-Bewertung zur Kontrolle der Strukturzunahme. Bayesian scoring to control tree growth. Optimization of binning for continuous attributes. Dynamische Gruppierung von Eingabewerten zur Bestimmung der wichtigsten Werte.
Dynamic grouping of input values to determine the most important values. Der Microsoft Decision Trees-Algorithmus ist schnell und skalierbar und ist auf eine einfache Parallelisierung ausgelegt. Dies bedeutet, dass alle Prozessoren zusammenarbeiten, um ein einzelnes, konsistentes Modell zu erstellen.
The Microsoft Decision Trees algorithm is fast and scalable, and has been designed to be easily parallelized, meaning that all processors work together to build a single, consistent model. The combination of these characteristics makes the decision-tree classifier an ideal tool for data mining.
If performance constraints are severe, you might be able to improve processing time during the training of a decision tree model by using the following methods. However, if you do so, be aware that eliminating attributes to improve processing performance will change the results of the model, and possibly make it less representative of the total population.
Limit the number of items in association models to limit the number of trees that are built. Restrict the number of discrete values for any attribute to 10 or less. You might try grouping values in different ways in different models. For more information, see Data Profiling Task and Viewer. The Microsoft Microsoft Decision Trees algorithm supports parameters that affect the performance and accuracy of the resulting mining model. You can also set modeling flags on the mining model columns or mining structure columns to control the way that data is processed.
The following table describes the parameters that you can use with the Microsoft Microsoft Decision Trees algorithm. Controls the growth of the decision tree. A low value increases the number of splits, and a high value decreases the number of splits. Der Standardwert richtet sich nach der Anzahl von Attributen in einem bestimmten Modell und ist der nachstehenden Liste zu entnehmen: The default value is based on the number of attributes for a particular model, as described in the following list: For 1 through 9 attributes, the default is 0.
For 10 through 99 attributes, the default is 0. For or more attributes, the default is 0. Forces the algorithm to use the specified columns as regressors, regardless of the importance of the columns as calculated by the algorithm. This parameter is only used for decision trees that are predicting a continuous attribute. Durch Festlegen dieses Parameters wird der Algorithmus gezwungen, zu versuchen, das Attribut als Regressor zu verwenden. By setting this parameter, you force the algorithm to try to use the attribute as a regressor.
However, whether the attribute is actually used as a regressor in the final model depends on the results of analysis. You can find out which columns were used as regressors by querying the model content.
Defines the number of input attributes that the algorithm can handle before it invokes feature selection. Der Standardwert lautet The default is Legen Sie diesen Wert auf 0 fest, um die Funktionsauswahl zu deaktivieren. Set this value to 0 to turn off feature selection. Defines the number of output attributes that the algorithm can handle before it invokes feature selection. Determines the minimum number of leaf cases that is required to generate a split in the decision tree.
You may need to increase this value if the dataset is very large, to avoid overtraining. Determines the method that is used to calculate the split score. The following options are available: Der Standardwert ist 4 oder BDE. The default is 4, or BDE. For an explanation of these scoring methods, see Feature Selection.
Determines the method that is used to split the node. Der Standardwert lautet 3. The default is 3. The Microsoft Microsoft Decision Trees algorithm supports the following modeling flags.
When you create the mining structure or mining model, you define modeling flags to specify how values in each column are handled during analysis.