31005 Assignment 2 Full Name 12345678 Implementation of ID3 Decision Tree Algorithm Project link: https://colab.research.google.com/drive/17iKQhg2ho_ldyK9omPm_kvs3fCs2QhbZ? usp=sharing Introduction Decision Trees are commonly used supervised learning data models. If the data attributes consists of , a decision tree maps an input data sample of observed attribute values to the target of prediction: . The tree performs the mapping by … This report presents the implementation of a decision tree construction algorithm for binary classification problems, where … The machine learning algorithm to construct a decision tree takes … ID3 Algorithm The algorithm consists of XXX modules: entropy computation, information gain computation, … …
Entropy … Information Gain Information gain measures the amount of the reduced uncertainty about a random variable given the knowledge of another random variable . In the supervised learning problem, is the target of prediction, … The computation of information gain in ID3 algorithm is implemented as in myid3.py, line 45–60, “compute_info_gain”. A snippet of the essential computation shown below 45. def compute_info_gain([TODO]): 46. … 47. for v, fr in [TODO]: 48. … 55. [TODO] 56. 57. # [TO-ADD-COMMENTS] 58. return [TODO] Line… implements the sum-over -values given an (the attribute) in equation (2). X1, X2,…, Xp fT p fT : [X1,…, Xp]↦ Y fT Y X IG (Y; X ) := H(Y ) − ∑ xi∈ p(xi)(∑ yj∈ p(yj |xi)log 1 p(yj |xi) ) (2) Y Y X Link code with algorithm details. - Number equations for cross-ref - edit code number to the actual line number in your code (take care of omissions in the quote, too) - HINT: try "http://www.planetb.ca/syntax-highlight-word" to format your code. A PLAIN TEXT and Clickable link to colab (recommended) / github / other cloud-based code repository Your information Start with title, NO cover page. SECTION: A short introduction to the problem and the method. Better with your comments on why you have chosen this method to address the problem. SECTION: The algorithm technical details and your implementation. A file easily identified in your project, or a cell marked in your notebook, e.g. you can comment the cell in the first line by #[cell:ig-comp], and cross-ref here. SUBSECTIONs: Important aspects of the algorithm Recommendation and hints Mandatory Marking criteria 31005 Assignment 2 Full Name 12345678 Model Evaluation The model is evaluated on XXXX Dataset and YYYY Dataset. …
Data Preparation … Experiment Design and Evaluation In one round of test, the data is split into two parts for training and another for evaluation … The model is constructed on the training … There are a few hyper parameters to configure: XX, YY. … Evaluation Results … Conclusion … One or two datasets that are prepared in your project / notebook. SECTION: evaluation report. SUBSECTION: BRIEF intro of each dataset, including the observed attributes and task, the necessary preprocessing steps. No more than 100 words for each dataset (shorter the better). No figures/tables/plots. SUBSECTION: BRIEF intro of the overall design of the experiment and evaluation criteria and scheme. No more than 100 words. SUBSECTION: the result of training/test (using the criteria you have discussed in the previous subsection). Include minimal program print-outs/plots. SECTION: Conclusion GOOD: The report contains a clear brief introduction to the algorithm. The input/output data formats are clearly stated. The implementation is correct. The program is well commented, explaining how the computer program codes correspond to algorithm steps. The implementation can be easily accessed from a cloud- based service such as colab/Github and replicated/executed for assessment. The evaluation is clearly designed according to standard data analytics models (e.g. CRISP-DM). The report is clearly structured and well written. ———— PASS: The report contains an introduction to the algorithm. The input/output data formats are listed. The implementation is mostly correct. The program is commented to be readable. The implementation can be easily accessed from a cloud-based service such as colab/Github and replicated/executed for assessment. The implementation may contain minor issues that can be addressed by a tutor without major revision. The report is written in clear English and is structured. ———— FAIL: The report misses an introduction to the algorithm, or the input/output data formats are not mentioned. The implementation cannot be replicated. The report is not in English or has no clear structure.
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