Koos van Strien moves from Python to R to run an xgboost algorithm:

Note that the parameters of xgboost used here fall in three categories:

  • General parameters

    • nthread (number of threads used, here 8 = the number of cores in my laptop)
  • Booster parameters

    • max.depth (of tree)
    • eta
  • Learning task parameters

    • objective: type of learning task (softmax for multiclass classification)
    • num_class: needed for the “softmax” algorithm: how many classes to predict?
  • Command Line Parameters

    • nround: number of rounds for boosting

Read the whole thing.

Related Posts

When Image Classifiers Look At Unknown Objects

Pete Warden explains that image classifiers aren’t magic: As people, we’re used to being able to classify anything we see in the world around us, and we naturally expect machines to have the same ability. Most models are only trained to recognize a very limited set of objects though, such as the 1,000 categories of the […]

Read More

Building Recurrent Neural Networks Using TensorFlow

Ahmet Taspinar walks us through creating a recurrent neural network topology using TensorFlow: As we have also seen in the previous blog posts, our Neural Network consists of a tf.Graph() and a tf.Session(). The tf.Graph() contains all of the computational steps required for the Neural Network, and the tf.Session is used to execute these steps. The computational steps defined in the tf.Graph can be […]

Read More


September 2016
« Aug Oct »