Below I have attached the workspace as well as the full specPrunes this tree to the given depth. Each pruned subtree is replaced with a new node representing the subtree’s majority label. For example, pruning the above decision tree to depth 1 would result in the following structure.
Implement a TextClassifier data type, a binary decision tree for classifying text documents.
Constructs a new TextClassifier given a fitted vectorizer for transforming data points and a splitter for determining the splits in the tree.
boolean classify(String text)
Returns a boolean representing the predicted label for the given text.
Prints a Java code representation of this decision tree in if/else statement format without braces and with 1 additional indentation space per level in the decision tree. Leaf nodes should print “return true;” or “return false;” depending on the label value.
if (vector <= 5.273602694890837)
if (vector <= 5.093549034038833)
if (vector <= 5.313808994135437)
Prunes this tree to the given depth. Each pruned subtree is replaced with a new node representing the subtree’s majority label. For example, pruning the above decision tree to depth 1 would result in the following structure.
if (vector <= 5.273602694890837)
Modify and run the Main class to debug your TextClassifier. Several classes and interfaces are included to handle the machine learning components of the TextClassifier. The most useful methods are described below.
The Vectorizer interface defines algorithms for transforming English text into a double design matrix for the Splitter interface. A design matrix consists of an array of double vectors, where each vector represents a single example text from the dataset (such as a comment or an email). The choice of BM25Vectorizer or LSAVectorizer algorithm determines how the English text is converted into a numeric representation.
double transform(String… texts)
Returns the design matrix for the given texts.
When implementing the classify method, given a single text, vectorizer.transform(text) evaluates to a vector that can be passed to Split.goLeft.
The Splitter interface provides a split method for dividing the given data points into left and right. The RandomSplitter is provided for debugging and visualization purposes while the GiniSplitter computes the optimal split based on information theory.
Returns the best split and the left and right splitters, or null if no good split exists. The Splitter.Result class represents the left and right splitters that result from applying a split.
Returns the majority label for this splitter.
When constructing a TextClassifier, use the Splitter.Result to recursively determine all of the decision rules in the decision tree. If the Splitter.Result is null, construct a new leaf node containing only the majority label.
The Split class represents a decision rule for splitting vector data.
boolean goLeft(double vector)
Returns true if and only if the given vector lies to the left of this decision rule.
Returns a string representation of this decision rule, vector[index] <= threshold.
Data structure errors
Not using x = change(x) when appropriate to simplify code.
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