This article provides an overview of the many algorithms for modeling in DataRobot, from classical methods such as linear regression, and random forest, to the latest deep learning methods from TensorFlow and Keras. DataRobot employs Deep Learning algorithms for regression and classification for Automated Machine Learning, Visual AI, and Automated Time Series.
Automated Machine Learning
Here we have AutoML which includes algorithms from Keras and TensorFlow. It supports multimodal deep learning, which means you can include numeric, categorical, text, and image data, all in the same model.
Figure 1. AutoML Leaderboard
Figure 2. AutoML Deep Learning
DataRobot also allows you to tune all of these deep learning models just as you would any of the machine learning models inside DataRobot. You can tune the number of layers, neurons per layer, activation functions, normalization, dropout rates, cell types, and more.
Figure 3. Advanced Tuning
Figure 4. Advanced Tuning parameters
Here is a partial list of the deep learning algorithms inside DataRobot.
Feedforward Neural Networks
Deep Residual Networks
Self-Normalizing Neural Networks
Adaptive Learning Networks
Attention-based text mining networks
State-of-the-art CNN architectures for images
Pretrained CNN architectures for images
Self-normalizing residual networks
Neural Architecture Search (using Hyperband)
Deep and Cross Networks
Neural Factorization Machines
AutoInt, short for Automatic Feature Interaction Learning
In Visual AI, DataRobot employs several pretrained CNN architectures, which allow you to start building models with just a few hundred images.
Figure 5. Visual AI Leaderboard
Let's look at some DataRobot’s pretrained CNNs.
Figure 6. Visual AI pretrained CNNs
Automated Time Series
Automated Time Series offers deep learning models in the form of Deep Learning Regressors, MultiSeries networks, Sequence to Sequence models, LSTM & GRU RNNs, along with the latest deep learning algorithm, DeepAR.
Here we can see some of the deep learning options available for time series forecasting.
Figure 7. Automated Time Series Deep Learning
If you have any questions, click Comment (below) and ask them now.