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.
npx skills add ECNU-ICALK/AutoSkill --skill deep-learning-prediction-with-chaid-and-time-series-splitting --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 Sea
- Prompt
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What does the Deep Learning Prediction with CHAID and Time-Series Splitting skill do?
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.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill deep-learning-prediction-with-chaid-and-time-series-splitting --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.