There are several things wrong in the code you posted: The keys of the param_grid dictionary need to be strings. You should be getting a NameError. The key “abc__n_estimators” should just be “n_estimators”: you are probably mixing this with the pipeline syntax. Here nothing tells Python that the string “abc” represents your AdaBoostClassifier. None (and … Read more
Each instance of RandomForestClassifier has an estimators_ attribute, which is a list of DecisionTreeClassifier instances. The documentation shows that an instance of DecisionTreeClassifier has a tree_ attribute, which is an instance of the (undocumented, I believe) Tree class. Some exploration in the interpreter shows that each Tree instance has a max_depth parameter which appears to … Read more
This is explained in the documentation The problem of learning an optimal decision tree is known to be NP-complete under several aspects of optimality and even for simple concepts. Consequently, practical decision-tree learning algorithms are based on heuristic algorithms such as the greedy algorithm where locally optimal decisions are made at each node. Such algorithms … Read more
The problem I can’t figure out is at the end you’ll have k Decision trees that could all be slightly different because they might not split the same way, etc. Which tree do you pick? The purpose of cross validation is not to help select a particular instance of the classifier (or decision tree, or … Read more
The value line in each box is telling you how many samples at that node fall into each category, in order. That’s why, in each box, the numbers in value add up to the number shown in sample. For instance, in your red box, 91+212+113=416. So this means if you reach this node, there were … Read more
Updated Answer with collapsible graph using d3js in Jupyter Notebook Start of 1st cell in notebook %%html <div id=”d3-example”></div> <style> .node circle { cursor: pointer; stroke: #3182bd; stroke-width: 1.5px; } .node text { font: 10px sans-serif; pointer-events: none; text-anchor: middle; } line.link { fill: none; stroke: #9ecae1; stroke-width: 1.5px; } </style> End of 1st cell … Read more
Decision Tree implementations differ primarily along these axes: the splitting criterion (i.e., how “variance” is calculated) whether it builds models for regression (continuous variables, e.g., a score) as well as classification (discrete variables, e.g., a class label) technique to eliminate/reduce over-fitting whether it can handle incomplete data The major Decision Tree implementations are: ID3, or … Read more
Directly from the documentation ( http://scikit-learn.org/0.12/modules/tree.html ): from io import StringIO out = StringIO() out = tree.export_graphviz(clf, out_file=out) StringIO module is no longer supported in Python3, instead import io module. There is also the tree_ attribute in your decision tree object, which allows the direct access to the whole structure. And you can simply read … Read more
Here is one liner for those who are using jupyter and sklearn(18.2+) You don’t even need matplotlib for that. Only requirement is graphviz pip install graphviz than run (according to code in question X is a pandas DataFrame) from graphviz import Source from sklearn import tree Source( tree.export_graphviz(dtreg, out_file=None, feature_names=X.columns)) This will display it in … Read more