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DeepLearning__L1L1 regularization (can add stability and improve generalization, causes many weights to become 0)default: 1.0E-5
DeepLearning__L2L2 regularization (can add stability and improve generalization, causes many weights to be smalldefault: 0.0
DeepLearning__activationThe activation function (non-linearity) to be used the neurons in the hidden layers. Tanh: Hyperbolic tangent function (same as scaled and shifted sigmoid). Rectifier: Chooses the maximum of (0, x) where x is the input value. Maxout: Choose the maximum coordinate of the input vector. With Dropout: Zero out a random user-given fraction of the incoming weights to each hidden layer during training, for each training row. This effectively trains exponentially many models at once, and can improve generalization.default: Rectifier
DeepLearning__adaptive_rateAdaptive learning ratedefault: true
DeepLearning__compute_variable_importancesCompute variable importances for input features (Gedeon method) - can be slow for large networksdefault: false
DeepLearning__distribution_functionDistribution function.default: AUTO
DeepLearning__early_stoppingIf true, parameters for early stopping needs to be specified.default: false
DeepLearning__epochsHow many times the dataset should be iterated (streamed), can be fractionaldefault: 10.0
DeepLearning__epsilonThe optimization is stopped if the training error gets below this epsilon value.default: 1.0E-8
DeepLearning__expert_parametersAdvanced parameters that can be set.
DeepLearning__expert_parameters_Advanced parameters that can be set.
DeepLearning__hidden_dropout_ratiosA fraction of the inputs for each hidden layer to be omitted from training in order to improve generalization. Defaults to 0.5 for each hidden layer if omitted.
DeepLearning__hidden_layer_sizesDescribes the size of all hidden layers.default: 50,50
DeepLearning__learning_rateThe learning rate, alpha. Higher values lead to less stable models, while lower values lead to slower convergence. Default is 0.005default: 0.005
DeepLearning__learning_rate_annealingLearning rate annealing reduces the learning rate to "freeze" into local minima in the optimization landscape. The annealing rate is the inverse of the number of training samples it takes to cut the learning rate in half (e.g., 1e-6 means that it takes 1e6 training samples to halve the learning rate). This parameter is only active if adaptive learning rate is disabled.default: 1.0E-6
DeepLearning__learning_rate_decayThe learning rate decay parameter controls the change of learning rate across layers. For example, assume the rate parameter is set to 0.01, and the rate_decay parameter is set to 0.5. Then the learning rate for the weights connecting the input and first hidden layer will be 0.01, the learning rate for the weights connecting the first and the second hidden layer will be 0.005, and the learning rate for the weights connecting the second and third hidden layer will be 0.0025, etc. This parameter is only active if adaptive learning rate is disabled.default: 1.0
DeepLearning__local_random_seedSpecifies the local random seeddefault: 1992
DeepLearning__loss_functionLoss function.default: Automatic
DeepLearning__max_runtime_secondsMaximum allowed runtime in seconds for model training. Use 0 to disable.default: 0
DeepLearning__max_w2Constraint for squared sum of incoming weights per unitdefault: 10.0
DeepLearning__missing_values_handlingHandling of missing values. Either Skip or MeanImputation.default: MeanImputation
DeepLearning__momentum_rampThe momentum_ramp parameter controls the amount of learning for which momentum increases (assuming momentum_stable is larger than momentum_start). The ramp is measured in the number of training samples. This parameter is only active if adaptive learning rate is disabled.default: 1000000.0
DeepLearning__momentum_stableThe momentum_stable parameter controls the final momentum value reached after momentum_ramp training samples. The momentum used for training will remain the same for training beyond reaching that point. This parameter is only active if adaptive learning rate is disabled.default: 0.0
DeepLearning__momentum_startThe momentum_start parameter controls the amount of momentum at the beginning of training. This parameter is only active if adaptive learning rate is disabled.default: 0.0
DeepLearning__nesterov_accelerated_gradientThe Nesterov accelerated gradient descent method is a modification to traditional gradient descent for convex functions. The method relies on gradient information at various points to build a polynomial approximation that minimizes the residuals in fewer iterations of the descent. This parameter is only active if adaptive learning rate is disabled.default: true
DeepLearning__reproducible_(uses_1_thread)Force reproducibility on small data (WARNING: will be slow - only uses 1 thread).default: false
DeepLearning__rhoIt is similar to momentum and relates to the memory to prior weight updates. Typical values are between 0.9 and 0.999.default: 0.99
DeepLearning__standardizeIf enabled, automatically standardize the data. If disabled, the user must provide properly scaled input data.default: true
DeepLearning__stopping_metricMetric to use for early stopping (AUTO: logloss for classification, deviance for regression)default: AUTO
DeepLearning__stopping_roundsEarly stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events.default: 1
DeepLearning__stopping_toleranceRelative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much).default: 0.001
DeepLearning__train_samples_per_iterationThe number of training data rows to be processed per iteration. Note that independent of this parameter, each row is used immediately to update the model with (online) stochastic gradient descent. This parameter controls the frequency at which scoring and model cancellation can happen. Special values are 0 for one epoch per iteration, -1 for processing the maximum amount of data per iteration. Special value of -2 turns on automatic mode (auto-tuning).default: -2
DeepLearning__use_local_random_seedIndicates if a local random seed should be used.default: false


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