Defining A Task |
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Define a Machine Learning Task |
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Specify variable type |
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Finding Learners |
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List sl3 Learners |
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sl3 Learners |
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Harmonic Regression |
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Univariate ARIMA Models |
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BART Machine Learner |
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Base Class for all sl3 Learners. |
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Bidirectional Long short-term memory Recurrent Neural Network (LSTM) |
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Conditional Density Estimation |
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Fit/Predict a learner with Cross Validation |
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Define interactions terms |
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Exponential Smoothing |
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Generalized Linear Models |
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Computationally Efficient GLMs |
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GLMs with Elastic Net Regularization |
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h2o Model Definition |
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Grid Search Models with h2o |
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The Scalable Highly Adaptive LASSO |
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Classification from Binomial Regression |
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Long short-term memory Recurrent Neural Network (LSTM) |
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Fitting Intercept Models |
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Non-negative Linear Least Squares |
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Optimize Metalearner according to Loss Function using optim |
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Principal Component Analysis and Regression |
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sl3 Learner wrapper for condensier |
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Random Forests |
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Ranger - A Fast Implementation of Random Forests |
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Learner for Recursive Partitioning and Regression Trees. |
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Univariate GARCH Models |
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SuperLearner Algorithm |
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Nonlinear Optimization via Augmented Lagrange |
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Nonlinear Optimization via Augmented Lagrange |
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Learner with Covariate Subsetting |
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Support Vector Machines |
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Nonlinear Time Series Analysis |
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xgboost: eXtreme Gradient Boosting |
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Use SuperLearner Wrappers, Screeners, and Methods, in sl3 |
Composing Learners |
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Pipeline (chain) of learners. |
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Learner Stacking |
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Customize chaining for a learner |
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Loss functions |
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Loss Function Definitions |
Risk Esimation |
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Metalearner functions |
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Combine predictions from multiple learners |
Helpful for Defining Learners |
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Generate a file containing a template |
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Get all args of parent call (both specified and defaults) as list |
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Call with filtered argument list |
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Estimate object size using serialization |
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dim that works for vectors too |
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Learner helpers |
Sample Datasets |
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Subset of growth data from the collaborative perinatal project (CPP) |
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Subset of growth data from the collaborative perinatal project (CPP) |
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Bicycle sharing time series dataset |
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Simulated data with continuous exposure |
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Miscellaneous |
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Querying/setting a single |
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Index |
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Customize chaining for a learner |
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Harmonic Regression |
|
Univariate ARIMA Models |
|
BART Machine Learner |
|
Base Class for all sl3 Learners. |
|
Bidirectional Long short-term memory Recurrent Neural Network (LSTM) |
|
Conditional Density Estimation |
|
Fit/Predict a learner with Cross Validation |
|
Define interactions terms |
|
Exponential Smoothing |
|
Generalized Linear Models |
|
Computationally Efficient GLMs |
|
GLMs with Elastic Net Regularization |
|
h2o Model Definition |
|
Grid Search Models with h2o |
|
The Scalable Highly Adaptive LASSO |
|
Classification from Binomial Regression |
|
Long short-term memory Recurrent Neural Network (LSTM) |
|
Fitting Intercept Models |
|
Non-negative Linear Least Squares |
|
Optimize Metalearner according to Loss Function using optim |
|
Principal Component Analysis and Regression |
|
sl3 Learner wrapper for condensier |
|
Random Forests |
|
Ranger - A Fast Implementation of Random Forests |
|
Learner for Recursive Partitioning and Regression Trees. |
|
Univariate GARCH Models |
|
SuperLearner Algorithm |
|
Nonlinear Optimization via Augmented Lagrange |
|
Nonlinear Optimization via Augmented Lagrange |
|
Learner with Covariate Subsetting |
|
Support Vector Machines |
|
Nonlinear Time Series Analysis |
|
xgboost: eXtreme Gradient Boosting |
|
Pipeline (chain) of learners. |
|
Learner Stacking |
|
|
Use SuperLearner Wrappers, Screeners, and Methods, in sl3 |
Get all args of parent call (both specified and defaults) as list |
|
Bicycle sharing time series dataset |
|
Subset of growth data from the collaborative perinatal project (CPP) |
|
Subset of growth data from the collaborative perinatal project (CPP) |
|
Simulated data with continuous exposure |
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Convert Factors to indicators |
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Learner helpers |
List sl3 Learners |
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Loss Function Definitions |
Make a stack of sl3 learners |
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Combine predictions from multiple learners |
Pack multidimensional predictions into a vector (and unpack again) |
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Predict Class from Predicted Probabilities |
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Plot predicted and true values for diganostic purposes |
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Risk Esimation |
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dim that works for vectors too |
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Querying/setting a single |
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Define a Machine Learning Task |
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Estimate object size using serialization |
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Undocumented Learner |
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Specify variable type |
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Generate a file containing a template |