As is implied by the names "Tree" and "Forest," the two concepts are similar. A Random Forest is essentially a collection of Decision Trees. A decision tree is built on an entire dataset, using all the features/variables of interest, whereas a random forest randomly selects observations/rows and specific features/variables to build multiple decision trees from and then averages the results.
- When you want your model to be simple and explainable.
- When you want non parametric model.
- When you don't want to worry about feature selection or regularization or worry about multi-collinearity.
- You can overfit the tree and build a model if you are sure of validation or test data set is going to be subset of training data set or almost overlapping instead of unexpected.
- When you don't bother much about interpreting the model but want better accuracy.
- Random forest will reduce variance part of error rather than bias part, so on a given training data set decision tree may be more accurate than a random forest. But on an unexpected validation data set, Random forest always wins in terms of accuracy.
- tf.Graph()
- tf.Operation()
- tf.Tensor()
- tf.Session()
- tf.get_default_session()
- tf.get_default_graph()
- tf.reset_default_graph()
- ops.reset_default_graph()
- tf.device("/cpu:0")
- tf.name_scope(value)
- GradientDescentOptimizer
- AdagradOptimizer
- AdagradOptimizer
- MomentumOptimizer
- AdamOptimizer
- FtrlOptimizer
- RMSPropOptimizer
- reduce_sum
- reduce_prod
- reduce_min
- reduce_max
- reduce_mean
- reduce_all
- reduce_any
- accumulate_n
- Activation Functins
- tf.nn?
- relu
- relu6
- elu
- softplus
- dropsign
- dropout
- bias_add
- sigmoid
- tanh
- sigmoid_cross_entropy_with_logits
- softmax
- log_softwax
- softwax_tf.convert_to_tensor(value)
- TensorFlowClassifier
- TensorFlowRegressor
- TensorFlowDNNRegressor
- TensorFlowLinearClassifier
- TensorFlowLinearRegressor
- TensorFlowRNNClassifier
- TensorFlowRNNRegressor