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Chapter 3
The graph shows the flow of data, ops, and nodes used in the whole process. To get
the TensorBoard graph for the estimator, you just need to define model_dir while
instantiating the Estimator class:
linear_est = tf.estimator.LinearRegressor(feature_columns=feature_
columns, model_dir = 'logs/func/')
Classification tasks and decision
boundaries
In the preceding section, we learned about the task of regression or prediction. In
this section we will talk about another important task: the task of classification. Let
us first understand the difference between regression (also sometimes referred to
as prediction) and classification:
• In classification the data is grouped into classes/categories, while in
regression the aim is to get a continuous numerical value for given data.
• For example, identifying the number of handwritten digits is a classification
task; all handwritten digits will belong to one of the ten numbers lying
between [0-9]. The task of predicting the price of the house depending upon
different input variables is a regression task.
• In the classification task, the model finds the decision boundaries separating
one class from another. In the regression task, the model approximates
a function that fits the input-output relationship.
• Classification is a subset of regression; here we are predicting classes.
Regression is much more general.
The following figure shows how the two classification and regression tasks differ. In
classification we need to find a line (or a plane or hyperplane in multidimensional
space) separating the classes.
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