DecisionTreeClassifier | C4.5 implementation using python | Data Manipulation library

In my dataframe highlighting product sales on the internet, I have a column that contains the description of each product sold.

I would like to create an algorithm to check if the combination and or redundancy of words has a correlation with the number of sales.

But I would like to be able to filter out words that are too redundant like the product type. For example, my dataframe deals with the sale of wines, so the algorithm must not take into account the word "wine" in the description.

In my df I have 700 rows consisting of 4 columns:

• product_id: id for each product
• product_price: product price
• total_sales: total number of product sales
• product_description: product description (e.g.: "Fruity wine, perfect as a starter"; "Dry and full-bodied wine"; "Fresh and perfect wine as a starter"; "Wine combining strength and character"; "Wine with a ruby ​​color, full-bodied "; etc...)

Edit: I added:

• the column 'CA': the total sales by product * the product's price
• an example of my df

My DataFrame example:

I'm trying to calculate some metrics using StratifiedKFold cross validation.

I have a piece of code written in julia with the goal of implementing the ID3 algorithm. But it has some bugs. I don't know how to fix it. Hope your help.

I have built a number of sklearn classifier models to perform multi-label classification and I would like to calibrate their predict_proba outputs so that I can obtain confidence scores. I would also like to use metrics such as sklearn.metrics.recall_score to evaluate them.

I have 4 labels to predict and the true labels are multi-hot encoded (e.g. [0, 1, 1, 1]). As a result, CalibratedClassifierCV does not directly accept my data:

Good evening to all

The objective from this post is to be able to plot the decision tree from the random decision tree process. After running the different options I always got the next error: 'RandomForestClassifier' object has no attribute 'tree_'

Really appreciate any help / code examples / ideas or links in oder to be able to solve this situation.

On the next set of code how I was able to plot the regular / normal decision tree.

I need to visualize a decision tree in dtreeviz in Databricks. The code seems to be working fine. However, instead of showing the decision tree it throws the following:

Out[23]:

Running the following code:

I have a pipeline that trains a decision tree. I would like to output the features that were used after the successful training and then I would like to display my decision tree. However, the following error occurs: AttributeError: 'GridSearchCV' object has no attribute 'n_features_'

• How can I display the relevant features that were used during the training?
• How can I create the decision tree?

I am working on an ID3 algorithm implementation. The issue that I am running into is processing the branches from the new root attribute

As the print shows

I just want to map the categorical features to numeric features.

when I just use continuous features for prediction, the decision tree works well.

however, after I replace these features, there are some error.

the df.info() gets as follows,