- Related Questions & Answers
- How can Tensorflow be used with estimators to visualize the titanic data?
- How can Tensorflow and Estimator be used to find the ROC curve on titanic dataset?
- How can Tensorflow be used with Estimators to explore the titanic data?
- How can Tensorflow be used to visualize the data using Python?
- How can Tensorflow be used with Estimators to perform data transformation?
- How can Tensorflow be used with Estimators to optimize the model?
- How can Tensorflow be used to visualize the augmented data from the dataset?
- How can Tensorflow be used with Estimators to split the iris dataset?
- How can Tensorflow and pre-trained model be used to visualize the data using Python?
- How can Tensorflow and pre-trained model after recompiling be used to visualize the data?
- How can Tensorflow be used with Estimators for feature engineering the model?
- How can Tensorflow be used with Estimators to evaluate the model using Python?
- How can Tensorflow be used with Estimators to display metadata about the dataset?
- How can Tensorflow be used to visualize the results of the model?
- How can Tensorflow be used with Estimators to define a function that shuffles data?

- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who

The titanic dataset model can be visualized and the ROC curve can be visualized to understand the performance with the help of the ‘matplotlib’ and ‘roc_curve’ (which is present in the ‘sklearn.metrics’ module) methods respectively.

**Read More:**
What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?

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.

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.

from sklearn.metrics import roc_curve from matplotlib import pyplot as plt print("The ROC curve, the true positive rate, and the false positive rate are plotted") fpr, tpr, _ = roc_curve(y_eval, probs) plt.plot(fpr, tpr) plt.title('ROC curve') plt.xlabel('false positive rate') plt.ylabel('true positive rate') plt.xlim(0,) plt.ylim(0,)

Code credit −https://www.tensorflow.org/tutorials/estimator/linear

The ROC curve, the true positive rate, and the false positive rate are plotted (0.0, 1.05)

- The receiver operating characteristic (ROC) is visualized.
- This gives an idea about the tradeoff between the true positive rate and false positive rate.

Advertisements