Decision-Tree | CART Decision Tree | Rule Engine library

 by   RRdmlearning Python Version: Current License: No License

kandi X-RAY | Decision-Tree Summary

kandi X-RAY | Decision-Tree Summary

Decision-Tree is a Python library typically used in Server, Rule Engine applications. Decision-Tree has no bugs, it has no vulnerabilities and it has low support. However Decision-Tree build file is not available. You can download it from GitHub.

CART Decision Tree
Support
    Quality
      Security
        License
          Reuse

            kandi-support Support

              Decision-Tree has a low active ecosystem.
              It has 22 star(s) with 24 fork(s). There are 3 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 0 open issues and 1 have been closed. On average issues are closed in 2 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of Decision-Tree is current.

            kandi-Quality Quality

              Decision-Tree has 0 bugs and 0 code smells.

            kandi-Security Security

              Decision-Tree has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              Decision-Tree code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              Decision-Tree does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              OutlinedDot
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              Decision-Tree releases are not available. You will need to build from source code and install.
              Decision-Tree has no build file. You will be need to create the build yourself to build the component from source.
              It has 212 lines of code, 13 functions and 1 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed Decision-Tree and discovered the below as its top functions. This is intended to give you an instant insight into Decision-Tree implemented functionality, and help decide if they suit your requirements.
            • Builds decision tree
            • Split two lists
            • Calculate difference count for each type in dataset
            • Return a graph representation of the decision tree
            • Plot the decision tree
            • Prune the tree based on the given value
            • Load a CSV file
            • Calculate GINI
            Get all kandi verified functions for this library.

            Decision-Tree Key Features

            No Key Features are available at this moment for Decision-Tree.

            Decision-Tree Examples and Code Snippets

            No Code Snippets are available at this moment for Decision-Tree.

            Community Discussions

            QUESTION

            Python - decision tree in lightgbm with odd values
            Asked 2022-Feb-18 at 17:55

            I am trying to fit a single decision tree using the Python module lightgbm. However, I find the output a little strange. I have 15 explanatory variables and the numerical response variable has the following characteristic:

            ...

            ANSWER

            Answered 2022-Feb-18 at 17:29

            As you may know LightGBM does a couple of tricks to speed things up. One of them is feature binning, where the values of the features are assigned to bins to reduce the possible number of splits. By default this number is 3, so for example if you have 100 samples you'd have about 34 bins.

            Another important thing here when using a single tree is that LightGBM does boosting by default, which means that it will start from an initial score and try to gradually improve on it. That gradual change is controlled by the learning_rate which by default is 0.1, so the predictions from each tree are multiplied by this number and added to the current score.

            The last thing to consider is that the tree size is controlled by num_leaves which is 31 by default. If you want to fully grow the tree you have to set this number to your number of samples.

            So if you want to replicate a full-grown decision tree in LightGBM you have to adjust these parameters. Here's an example:

            Source https://stackoverflow.com/questions/71158779

            QUESTION

            Named R-chunk can't be referd to
            Asked 2022-Jan-18 at 19:21

            The following MWE should correctly display the number of the figure by referring to it with \@ref(fig:chunk-label) yet the reference is not found by the function. Is there any option that I have to add to the chunk header to achieve a correct reference?

            MWE :

            ...

            ANSWER

            Answered 2022-Jan-18 at 19:21

            The issue is quite subtle. To make your reference work you have to add a line break after the code chunk and the following text:

            Source https://stackoverflow.com/questions/70760568

            QUESTION

            Create dropdown menu that replaces href with a certain json file
            Asked 2022-Jan-18 at 14:21

            Context: I'm trying to create a simply project (a decision tree) and I'd like to know how could I create a drop down menu so that the user can select a specific option and retrieve a output from a json file.

            This is the HTML code:

            ...

            ANSWER

            Answered 2022-Jan-18 at 14:21

            To listen to change events on your select element you can observe changes like so:

            Source https://stackoverflow.com/questions/70755883

            QUESTION

            How to change colors for decision tree plot using sklearn plot_tree?
            Asked 2021-Dec-27 at 14:35

            How to change colors in decision tree plot using sklearn.tree.plot_tree without using graphviz as in this question: Changing colors for decision tree plot created using export graphviz?

            ...

            ANSWER

            Answered 2021-Dec-27 at 14:35

            Many matplotlib functions follow the color cycler to assign default colors, but that doesn't seem to apply here.

            The following approach loops through the generated annotation texts (artists) and the clf tree structure to assign colors depending on the majority class and the impurity (gini). Note that we can't use alpha, as a transparent background would show parts of arrows that are usually hidden.

            Source https://stackoverflow.com/questions/70437840

            QUESTION

            Drools: When using JPAWorkingMemoryDbLogger, the processInstanceId is always 1
            Asked 2021-Dec-17 at 09:58

            We've been executing a JBPM decision tree in memory for our call center. This works great, but we'd really like to be able to render diagrams in our BusinessCentral instance. This means we have to add JPAWorkingMemoryDbLogger so it logs stuff out to the drools tables. We're not using kie-server to execute our JBPM, but executing it in the follow code.

            What we're finding is that every process instance id is 1, whereas other things JBPM we execute via kie-server manage to get an incremented PID.

            What do we need to change in the setup of the KieSession so it it increments the process instance id?

            ...

            ANSWER

            Answered 2021-Dec-17 at 09:58

            QUESTION

            Memory error if not using Standard Scaler
            Asked 2021-Aug-23 at 05:09

            I have read here https://towardsdatascience.com/do-decision-trees-need-feature-scaling-97809eaa60c6 and watch https://www.youtube.com/watch?v=nmBqnKSSKfM&ab_channel=KrishNaik video which stated that you don't need to use Standard Scaler for Decision Tree machine learning.

            But, what happened is on my code is the opposite. Heres the code I am trying to run.

            ...

            ANSWER

            Answered 2021-Aug-23 at 03:13

            Decision Tree can work without Standard Scaler and with Standard Scaler. The important thing to note here is that scaling the data won't affect the performance of a Decision Tree model.

            If you are plotting the data afterwards though I imagine you don't want to plot the scaled data but rather the original data; hence your problem.

            The simplest solution I can think of for doing this is to pass sparse=True as an argument to numpy.meshgrid as that seems to be what's throwing the error in your traceback. There's some detail on that in a past question here.

            So applied to your question, that would mean you change this line:

            Source https://stackoverflow.com/questions/68881786

            QUESTION

            ValueError in DecisionTreeClassifier
            Asked 2021-Jul-14 at 13:23

            Here's the link of the decision tree implementation I used. https://www.geeksforgeeks.org/decision-tree-implementation-python/

            And my dataframe is only composed of "A" and "B" with 512 values for each of them.

            ...

            ANSWER

            Answered 2021-Jul-14 at 13:22

            You have a problem with your y labels. If your model should predict if a sample belong to class A or B you should, according to your dataset, use the index as label y as follow since it contains the class ['A', 'B']:

            Source https://stackoverflow.com/questions/68378767

            QUESTION

            sklearn "Pipeline instance is not fitted yet." error, even though it is
            Asked 2021-Jun-11 at 23:28

            A similar question is already asked, but the answer did not help me solve my problem: Sklearn components in pipeline is not fitted even if the whole pipeline is?

            I'm trying to use multiple pipelines to preprocess my data with a One Hot Encoder for categorical and numerical data (as suggested in this blog).

            Here is my code, and even though my classifier produces 78% accuracy, I can't figure out why I cannot plot the decision-tree I'm training and what can help me fix the problem. Here is the code snippet:

            ...

            ANSWER

            Answered 2021-Jun-11 at 22:09

            You cannot use the export_text function on the whole pipeline as it only accepts Decision Tree objects, i.e. DecisionTreeClassifier or DecisionTreeRegressor. Only pass the fitted estimator of your pipeline and it will work:

            Source https://stackoverflow.com/questions/67943229

            QUESTION

            Why ctree is only returning a single terminal node in this case?
            Asked 2021-May-16 at 10:22

            Introduction

            I'm learning the basics of AI. I have created a .csv file with random data to test Decision Trees. I'm currently using R in Jupyther Notebook.

            Problem

            Temperature, Humidity and Wind are the variables which determine if you are allowed to fly or not.

            When I execute ctree(vuelo~., data=vuelo.csv) the output it's just a single node when I was expecting a full tree with the variables (Temperatura, Humdedad, Viento), as I resolved on paper.

            Snippet of the result

            The data used is the next table:

            ...

            ANSWER

            Answered 2021-May-16 at 10:22

            Answer

            ctree only creates splits if those reach statistical significance (see ?ctree for the underlying tests). In your case, none of the splits do so, and therefore no splits are provided.

            In your case, you could force a full tree by messing with the controls (see ?ctree and ?ctree_control), e.g. like this:

            Source https://stackoverflow.com/questions/67545994

            QUESTION

            Logo as site first heading according to WCAG
            Asked 2021-Mar-17 at 13:31

            My company's main page doesn't have a H1 and having content order in mind the best solution would be having the logo encapsuled inside the heading, although not ideal, it should be acceptable. Here's the code I have so far:

            ...

            ANSWER

            Answered 2021-Mar-17 at 13:31

            Would it be SEO friendly since the heading would come from the logo's alternative text?

            Should be fine. However as you will see there is a better way to structure this that will be better for SEO.

            Would it be better to put a aria-label="Company" and title="Company" within the link so the heading comes from there?

            No it will be more compatible the way you have it now. Don't use title it is useless for accessibility and nowadays more devices are touch based than pointer based so it doesn't serve much purpose there either.

            Or is this approach just not acceptable at all and I should use something else as the H1?

            The approach is acceptable (adding a hyperlink to a

            ) but your current implementation is not acceptable.

            The

            should describe the page you are currently on so that an end user knows they are in the correct place.

            Your alt attribute describes the logo, which is correct for the home page link but not useful to describe the page. (If a screen reader user uses shortcuts to read the page

            they will hear "Link, Company homepage". This would be confusing.)

            Also the other issue with this is that the company logo is nearly always used as a shortcut for "home", so you either end up breaking that convention on other pages (as you can't have a hyperlink saying "about us" that leads to the home page) or break convention be having the logo point to the current page.

            Neither of these are a good idea.

            So what are my options?

            Obviously as you stated a visual heading on the page would be best. This isn't just for users of assistive tech but also useful for everybody to help orientate them on the site. If you can make this work the advice is to do that. This is 10 times more effective than the next option.

            However assuming you cannot make a visible

            work on the page the next best thing would be a that is hidden using visually hidden text.

            This means that screen reader users can still access the

            without it changing the visual design. It also means you can leave the logo link to the homepage as it should be, in line with conventions and expected behaviour.

            Also because of the issues mentioned previously this should be separate and in a logical place in the document, such as the beginning of the element.

            Example

            Source https://stackoverflow.com/questions/66673683

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install Decision-Tree

            You can download it from GitHub.
            You can use Decision-Tree like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
            Find more information at:

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
            CLONE
          • HTTPS

            https://github.com/RRdmlearning/Decision-Tree.git

          • CLI

            gh repo clone RRdmlearning/Decision-Tree

          • sshUrl

            git@github.com:RRdmlearning/Decision-Tree.git

          • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link

            Explore Related Topics

            Consider Popular Rule Engine Libraries

            easy-rules

            by j-easy

            RulesEngine

            by microsoft

            NRules

            by NRules

            grule-rule-engine

            by hyperjumptech

            nools

            by noolsjs

            Try Top Libraries by RRdmlearning

            Machine-Learning-From-Scratch

            by RRdmlearningPython

            Random-Forest

            by RRdmlearningPython

            DecisionTree

            by RRdmlearningPython