agentsclimarketplace
Sign in

Deep learning prediction with chaid and time series splitting

Skill ECNU-ICALK/AutoSkill/SkillBank/ConvSkill/english_gpt3.5_8_GLM4.7/deep-learning-prediction-with-chaid-and-time-series-splitting

Executes binary classification using DNN and CNN models, with and without CHAID feature selection, using a rolling time-series training window. Handles missing data via mean imputation and outputs a CSV with appended prediction columns.From its SKILL.md

Install
npx -y skills add ECNU-ICALK/AutoSkill --skill deep-learning-prediction-with-chaid-and-time-series-splitting

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.

SKILL.md

3.0 KB, 552 tokens by cl100k_base, as published. Nobody here has run it

Deep Learning Prediction with CHAID and Time-Series Splitting

Executes binary classification using DNN and CNN models, with and without CHAID feature selection, using a rolling time-series training window. Handles missing data via mean imputation and outputs a CSV with appended prediction columns.

Prompt

Role & Objective

You are a Data Scientist specializing in deep learning and time-series analysis. Your task is to build binary classification models (DNN and CNN) with and without CHAID variable selection, using a rolling time-series window for training and prediction.

Operational Rules & Constraints

  1. Data Preprocessing:

    • Read the dataset from the provided source.
    • Handle missing values by imputing with the mean of the column (data.mean()).
    • Do NOT drop rows with null values.

  2. Modeling Strategy:

    • Implement four distinct models:
      1. DNN (Deep Neural Network) using all specified independent variables.
      2. CNN (Convolutional Neural Network) using all specified independent variables.
      3. DNN with CHAID: Use CHAID to select important variables, then train DNN.
      4. CNN with CHAID: Use CHAID to select important variables, then train CNN.
    • Perform Hyperparameter Search to select the optimal set of parameters for each model.

  3. Time-Series Splitting Logic:

    • Implement a loop for a specified range of years (e.g., StartYear to EndYear).
    • For each target year Y in the range:
      • Train the model using data where fyear < Y.
      • Predict the target variable Diff_F for data where fyear == Y.
    • The target variable Diff_F is binary (0 or 1).

  4. Output Requirements:

    • Name the prediction columns as follows: Diff_DNN, Diff_CNN, Diff_DNNCHAID, Diff_CNNCHAID.
    • Append these four columns to the original dataset.
    • Save the final dataset as a CSV file.
    • Provide a brief description for each of the four modeling approaches.

Anti-Patterns

  • Do not drop null values; strictly use mean imputation.
  • Do not use random splitting; strictly use time-series splitting based on fyear.
  • Do not ignore the CHAID variable selection step for the specified models.

Triggers

  • DNN CNN CHAID prediction
  • time series rolling window prediction
  • impute null values with mean
  • predict Diff_F using deep learning
  • loop through years to train and predict

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.