Boosted trees with Tensorflow can be used to show a sample of the titanic dataset using the ‘head’ method, the ‘describe’ method and the ‘shape’ method. The head method gives the first few rows of the dataset, and the describe method gives information about the dataset, such as column names, types, mean, variance, standard deviation and so on. The shape method gives the dimensions of the data.
We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.
A neural network that contains at least one layer is known as a convolutional layer. We can use the Convolutional Neural Network to build learning model.
We are using the Google Colaboratory to run the below code. Google Colab or Colaboratory helps run Python code over the browser and requires zero configuration and free access to GPUs (Graphical Processing Units). Colaboratory has been built on top of Jupyter Notebook.
We will see how a gradient boosting model can be trained using decision trees and tf.estimator API.
An Estimator is TensorFlow's high-level representation of a complete model. It is designed for easy scaling and asynchronous training. Estimators use feature columns to describe how the model would interpret the raw input features. An Estimator expects a vector of numeric inputs, and feature columns will help describe how the model should convert every feature in the dataset.
Boosted Trees models are considered the most popular and effective machine learning approaches for regression as well as classification. It is an ensemble technique which combines the predictions from many (10s or 100s or 1000s) tree models.
print("Some sample of the data") print(dftrain.head()) print("Metadata about the dataset") print(dftrain.describe()) print("Dimensions of the data") print(dftrain.shape, dfeval.shape)
Some sample of the data sex age n_siblings_spouses parch ... class deck embark_town alone 0 male 22.0 1 0 ... Third unknown Southampton n 1 female 38.0 1 0 ... First C Cherbourg n 2 female 26.0 0 0 ... Third unknown Southampton y 3 female 35.0 1 0 ... First C Southampton n 4 male 28.0 0 0 ... Third unknown Queenstown y [5 rows x 9 columns] Metadata about the dataset age n_siblings_spouses parch fare count 627.000000 627.000000 627.000000 627.000000 mean 29.631308 0.545455 0.379585 34.385399 std 12.511818 1.151090 0.792999 54.597730 min 0.750000 0.000000 0.000000 0.000000 25% 23.000000 0.000000 0.000000 7.895800 50% 28.000000 0.000000 0.000000 15.045800 75% 35.000000 1.000000 0.000000 31.387500 max 80.000000 8.000000 5.000000 512.329200 Dimensions of the data 627 264