Over the weekend a fellow software developer sent me an email message with a question regarding the JUnit Example post. Given that the interchange did not occur via the message section following all posts, I will not post the sequence of messages. The question was not regarding an existing test case in the TriangleTest class. What he was interested in is a new test case in which one could generate a text file with the information to run multiple (e.g., 100) test cases at once. If you are interested you can find the proposed solution in my GitHub repository. I post it as a second branch. I will not be merging branches due to the fact that this modification would not work with the original challenge. In addition, I did modify the test scaffolding on the Solution not to prompt for the lengths of the sides in the triangle. It is easier to copy and paste the sides that to be prompted for each side separately. Continue reading “TensorFlow 2.0 – Linux Installation”
Over the weekend decided to continue experimenting with TensorFlow during the next couple weeks. The first task was to make sure my installation was up to date.
One of the requirements for TensorFlow on Windows is to use Python 3.5 or higher. For some reason my previously installed Python version switched to 2.7. I believe that happened when using Conda after installing TensorFlow for the first time. I checked my machine and Python was installed in the C:\Program Files\Python35 folder. Nevertheless I decided to download the latest version of Python and reinstall it. Apparently it was the same: 3.5.2. Continue reading “Revisiting TensorFlow™”
In machine learning, a decision tree is a predictive model which maps observations about an item (represented in the branches) to conclusions about the item’s target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining and machine learning.
Tree models where the target variable can take a finite set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Continue reading “Decision Tree – Machine Learning”