Decision-Tree | Implementation of Decision Tree using ID3 Algorithm | Machine Learning library

by revantkumar Python Version: Current License: No License

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kandi X-RAY | Decision-Tree Summary

Decision-Tree is a Python library typically used in Artificial Intelligence, Machine Learning, Example Codes 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.

Part a) How you implemented the initial tree (Section A) and why you chose your approaches?. For implementing the decision tree, we have used the ID3 (Iterative Dichotomiser 3) Heuristic.

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Decision-Tree has a low active ecosystem.

It has 9 star(s) with 25 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

Decision-Tree has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Decision-Tree is current.

Quality

Decision-Tree has no bugs reported.

Security

Decision-Tree has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Decision-Tree does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

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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.

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.

Run decision tree .
Calculates the entropy of the given attributes .
Build a tree .
Computes the major class of the given attribute .
Calculate the info gain of the given attributes .
Get data from a list of attributes .
Choose the attribute with the given attributes .
Get a list of values from data .
Initialize the value .
Train the model .

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

Trending Discussions on Decision-Tree

sklearn "Pipeline instance is not fitted yet." error, even though it is

Why ctree is only returning a single terminal node in this case?

Logo as site first heading according to WCAG

What does the value list mean in a Decision Tree graph

Do features have to be float numbers for multiclass-classification by Decision Tree?

plotting a 3d graph of a regressor made with sklearn

Error in converting categorical variables to factor in R

Tidymodels Package: Visualising a random forest model using ggplot() to show the most important predictors

Tidymodels Package: Visualising Bagged Trees using ggplot() to show the most important predictors

Tidymodel Package: General linear models (glm) and decision tree (bagged trees, boosted trees, and random forest) models in R

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

QUESTION

What does the value list mean in a Decision Tree graph

Asked 2021-Mar-04 at 08:32

While viewing this question scikit learn - feature importance calculation in decision trees, I have trouble understanding the value list of the Decision Tree. For example, the top node has value=[1,3]. What exactly are 1 and 3? Does it mean if X[2]<= 0.5, then 1 false, 3 true? If so, the value list is [number of false cases, number of true cases]. If so, what about the value lists of the leaves?

Why do three right leaves have [0,1] and one left leaf has [1,0]?
What does [1,0] or [0,1] mean anyway? One false zero true or zero false one true? But there's no condition on the leaves (like something <=.5). Then what is true what is false?

Your advice is highly appreciated!

...

ANSWER

Answered 2021-Mar-04 at 08:32

value=[1,3] means that, in this exactly leaf of the tree (before applying the filter x[2] <=0.5), you have:

1 sample of the class 0
3 sample of the class 1

Once you are going down the tree, you are filtering. Your objective is have perfectly separated classes. So you tend to have something like value=[0,1], which means that after applying all filters, you have 0 samples of class 0 and 1 samples of class 1.

You can also check that the sum of value is always similar to the samples. This makes completely sense since value is only telling you how all samples that arrived this leaf are distributed.

Source https://stackoverflow.com/questions/66467206

QUESTION

Do features have to be float numbers for multiclass-classification by Decision Tree?

Asked 2020-Dec-30 at 13:52

X_train

------------------------------------------------------------------------------------------
   | bias | word.lower | word[-3:] | word.isupper | word.isdigit |  POS  |  BOS  |  EOS  |
------------------------------------------------------------------------------------------
0  |  1.0 | headache,  |      HE,  |         True |        False |   NNP |  True | False |
1  |  1.0 |    mostly  |      tly  |        False |        False |   NNP | False | False |
2  |  1.0 |       but  |      BUT  |         True |        False |   NNP | False | False |
...
...
...

y_train

------------
   |  OBI  |
------------
0  | B-ADR |
1  | O     |
2  | O     |
...
...
...

...

ANSWER

Answered 2020-Dec-30 at 13:52

Yes, they need to be numeric (not necessarily float). So if you have 4 distinct text labels in a column then you need to convert this to 4 numbers. To do this, use sklearn's labelencoder. If your data is in a pandas dataframe df,

Source https://stackoverflow.com/questions/65506053

QUESTION

plotting a 3d graph of a regressor made with sklearn

Asked 2020-Dec-10 at 10:15

I have been using this tutorial to learn decision tree learning, and am now trying to understand how it works with higher dimensional datasets.

Currently my regressor predicts a Z value for an (x,y) pair that you pass to it.

...

ANSWER

Answered 2020-Dec-04 at 00:05

Try this, I do not have all the packages installed, so I tested this on google colab. Let me know if this is what you expected.

Source https://stackoverflow.com/questions/65130595

QUESTION

Error in converting categorical variables to factor in R

Asked 2020-Nov-24 at 16:58

In this tutorial, I tried to use another method for converting categorical variables to factor.

In the article, the following method is used.

...

ANSWER

Answered 2020-Nov-24 at 16:58

as.factor((birthwt[cols])) is calling as.factor on a list of 5 vectors. If you do that R will interpret each of those 5 vectors as the levels, and the column headers as the labels, of a factor variable, which is clearly not what you want:

Source https://stackoverflow.com/questions/64990872

QUESTION

Tidymodels Package: Visualising a random forest model using ggplot() to show the most important predictors

Asked 2020-Nov-22 at 12:54

Overview

I am following a tutorial (see below) to find the best fit models from bagged trees, random forests, boosted trees, and general linear models.

Tutorial (see examples below)

https://bcullen.rbind.io/post/2020-06-02-tidymodels-decision-tree-learning-in-r/

Issue

In this case, I would like to explore the data further and visualise the most important predictors (see diagram below) for my data in the random forest model.

My data frame is called FID and the predictors in the random forest model involve:

Year (numeric)
Month (Factor)
Days (numeric)

The dependent variable is Frequency (numeric)

When I try to run the plot to visualise the most important predictor, I keep on getting this error message:-

...

ANSWER

Answered 2020-Nov-22 at 12:54

It was not about to extract the errors correctly, if you look at the tibble containing all the models:

Source https://stackoverflow.com/questions/64939433

QUESTION

Tidymodels Package: Visualising Bagged Trees using ggplot() to show the most important predictors

Asked 2020-Nov-20 at 19:55

Overview:

I am following a tutorial (see below) to find the best fit models from bagged trees, random forests, boosted trees and general linear models.

Tutorial (see examples below)

https://bcullen.rbind.io/post/2020-06-02-tidymodels-decision-tree-learning-in-r/

Issue

In this case, I would like to explore the data further and visualise the most important predictors (see diagram below) for my data.

My data frame is called FID and the predictors in the bagged tree model involve:

Year (numeric)
Month (Factor)
Days (numeric)

The dependent variable is Frequency (numeric)

When I try to run the plot to visualise the most important predictor, I keep on getting this error message:-

Error Message

...

ANSWER

Answered 2020-Nov-20 at 19:55

There are a couple of things you need to adjust here:

Be sure to extract what you need during fit_resamples()
Use the correct variable names for your data that you are creating in the bag_roots() function.

It will end up like this:

Source https://stackoverflow.com/questions/64928108

QUESTION

Tidymodel Package: General linear models (glm) and decision tree (bagged trees, boosted trees, and random forest) models in R

Asked 2020-Nov-19 at 02:31

Issue

I am attempting to undertake an analysis using the Tidymodels Package in R. I am following this tutorial below regarding decision tree learning in R:-

Tutorial

https://bcullen.rbind.io/post/2020-06-02-tidymodels-decision-tree-learning-in-r/

I have a data frame called FID (see below) where the dependent variable is the frequency (numeric), and the predictor variables are:- Year (numeric), Month (factor), Monsoon (factor), and Days (numeric).

I believe I have successfully followed the tutorial named "Tidymodels: Decision Tree Learning in R" by building a bagged tree, random forest, and boosted tree model.

For this analysis, I would also like to construct a general linear model (glm) in order to make model comparisons between all models (i.e the random forest, bagged tree, boosted tree, and general linear models) to establish the best model fit. All models are subject to 10-fold cross-validation to decrease the bias of overfitting.

Problem

Subsequently, I have attempted to adapt the code (please see below) from the tutorial to fit a glm model, but I am confused whether I have tuned the model appropriately. I am unsure if this element of glm R-code is producing the error message below when I am attempting to produce the rmse values after the models have all been fit:-

Error message

...

ANSWER

Answered 2020-Nov-18 at 20:58

I believe the error from fitting the linear model is coming from how Month and Monsoon are related to each other. They are perfectly correlated:

Source https://stackoverflow.com/questions/64892060

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 .

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