One package to train them all.
ModelMiner.jl aims to provide an easy use interface to train multiple machine learning
models with a single function all. It provides a mine(...) interface through
which users can train multiple classification and regression models easily. The goal
is to quickly verify which models performs better on data without much effort.
- Prepare your data
using MLJ, RDatasets
data = dataset("datasets", "iris")
y, X = MLJ.unpack(data, ==(:Species))- Call
mine(...)to train and evaluate models
using ModelMiner
results = mine(X, y)The output will a dataframe with performance measures according to the data
7×3 DataFrame
Row │ name Accuracy MulticlassFScore
│ String Float64 Float64
─────┼─────────────────────────────────────────────────────────────
1 │ AdaBoostStumpClassifier 0.94 0.927209
2 │ ConstantClassifier 0.266667 0.140233
3 │ DecisionTreeClassifier 0.966667 0.965303
4 │ DecisionTreeClassifier 0.92 0.918642
5 │ DeterministicConstantClassifier 0.266667 0.140063
6 │ KernelPerceptron 0.94 0.937882
7 │ LinearPerceptron 0.813333 0.771626
8 │ LinearSVC 0.96 0.963594
9 │ LogisticClassifier 0.973333 0.968547
10 │ MultinomialClassifier 0.953333 0.952808
11 │ NeuralNetworkClassifier 0.533333 0.405575
12 │ NuSVC 0.966667 0.96627
13 │ Pegasos 0.6 0.484509
14 │ RandomForestClassifier 0.953333 0.957148
15 │ RandomForestClassifier 0.953333 0.948225
16 │ SVC 0.96 0.957057
17 │ XGBoostClassifier 0.94 0.939973
- Prepare your data
data = dataset("MASS", "Boston")
y, X = MLJ.unpack(data, ==(:MedV))- Train models and evaluate models
using ModelMiner
results = mine(X, y)Results:
8×2 DataFrame
Row │ name RootMeanSquaredError
│ String Float64
─────┼──────────────────────────────────────────────────────
1 │ ConstantRegressor 9.22236
2 │ DecisionTreeRegressor 5.0619
3 │ DecisionTreeRegressor 4.51225
4 │ DeterministicConstantRegressor 9.19367
5 │ GaussianMixtureRegressor 8.18446
6 │ NeuralNetworkRegressor 20.2011
7 │ RandomForestRegressor 3.98226
8 │ RandomForestRegressor 3.38003
- Automated Feature scaling
- Hyperparameter Tuning
- Reporting performance statistics from Cross Validations
Thanks to the developers of LazyPredict for inspiration and @ablaom for helping me overcome some challenges in developing.