Machine Learning with TensorFlow
Dataset (CSV/XLS/XLSX)
Training Algorithm
What it is: Wide & Deep: combines a linear branch and deep branch, then merges both signals into one prediction.
Why it's unique: Balances memorization (wide) and generalization (deep), which is often stronger than either branch alone on mixed tabular feature sets.
Distillation Note: Supported.
Train TensorFlow Model
Epochs: 60
Batch sizes: 64
Learning rates: 0.001
Test sizes: 0.2
Hidden dims: 128
Hidden layers: 2
Dropouts: 0.1
Dataset: none
Planned runs: 1
Bayesian Optimization
What is it: A method for optimizing expensive black-box functions by using a probabilistic model to choose promising parameter settings.
How it works: Uses completed runs to suggest the next promising hyperparameter combination. Requires at least 5 completed runs for the specific algorithm.
Training Runs
No runs yet. Train once to populate the results table.
Preprocessing Notes
Categorical Encoding: Text columns with ≤ 20 unique values are automatically One-Hot Encoded.
High Cardinality & IDs: Text columns with > 20 unique values or ID-like names are dropped to prevent feature explosion.
Date Parsing: Dates and timestamps are extracted into Year, Month, and Day numeric features.
Missing Values: Missing numeric values are imputed using the column median to maintain robustness against outliers.
Feature Scaling: All features are standardized to zero mean and unit variance (StandardScaler) before analysis. This prevents large-range features from dominating algorithms like PCA.
Dataset Table Preview
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