text: Title: Multi-model metrics visualizer
  Body: **Describe the solution you'd like**
  
  I would like to create an at-a-glance representation of multiple model scores so that I can easily compare and contrast different model instances. This will be our first attempt handling multiple models in a visualizer - so could be tricky, and may require a new API. I envision something that creates a heatmap of metrics to models, sort of like the classification report, but where the rows are not classes but are instead are models. 
  
  I propose the code would look something like this:
  
  ```python
  viz = MultiModelMetrics([
      ("Naive Bayes", GaussianNB()),
      ("Neural Network", MultilayerPerceptron()),
      ("Logistic", LogisticRegression()),
      ("Boosting", GradientBoostingClassifier()), 
      ("Bagging", RandomForestClassifier()), 
  ], is_fitted=False, metrics="classification")
  
  viz.fit(X_train, y_train)
  viz.score(X_test, y_test)
  viz.show()
  ```
  
  Like a pipeline, this API allows us to specify names for the estimator that will be visualized, or a list of visualizers can be added and the estimator name will be used. 
  
  **Examples**
  
  A prototype example:
  
  ![multimodelscores](https://user-images.githubusercontent.com/745966/75354447-f94c4100-587a-11ea-887e-47ac13738eda.png)
  
  