decision-tree | Decision tree on Golang | Rule Engine library
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Decision tree on Golang
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decision-tree Key Features
decision-tree Examples and Code Snippets
def fit(self, X, Y):
current_node = self.root
depth = 0
queue = []
# origX = X
# origY = Y
while True:
if len(Y) == 1 or len(set(Y)) == 1:
# base case, only 1 sample
def train(self, X, y):
"""
train:
@param X: a one dimensional numpy array
@param y: a one dimensional numpy array.
The contents of y are the labels for the corresponding X values
train does not have a
def main():
Xtrain, Ytrain, Xtest, Ytest, word2idx = get_data()
# convert to numpy arrays
Xtrain = np.array(Xtrain)
Ytrain = np.array(Ytrain)
# convert Xtrain to indicator matrix
N = len(Xtrain)
V = len(word2idx) + 1
Community Discussions
Trending Discussions on decision-tree
QUESTION
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 17:29As 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:
QUESTION
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 19:21The issue is quite subtle. To make your reference work you have to add a line break after the code chunk and the following text:
QUESTION
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 14:21To listen to change events on your select
element you can observe changes like so:
QUESTION
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 14:35Many 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.
QUESTION
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 09:58You could try like this:
QUESTION
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 03:13Decision 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:
QUESTION
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 13:22You 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']
:
QUESTION
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 22:09You 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:
QUESTION
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.
The data used is the next table:
...ANSWER
Answered 2021-May-16 at 10:22Answer
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:
QUESTION
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 13:31Would 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
The
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
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
that is hidden using visually hidden text.
This means that screen reader users can still access the
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.
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