  Classification model selection is a process of identifying a suitable model
class for a given classification task on a dataset. Traditionally, model
selection is based on cross-validation, meta-learning, and user preferences,
which are often time-consuming and resource-intensive. The performance of any
machine learning classification task depends on the choice of the model class,
the learning algorithm, and the dataset's characteristics. Our work proposes a
novel method for automatic classification model selection from a set of
candidate model classes by determining the empirical model-fitness for a
dataset based only on its clustering indices. Clustering Indices measure the
ability of a clustering algorithm to induce good quality neighborhoods with
similar data characteristics. We propose a regression task for a given model
class, where the clustering indices of a given dataset form the features and
the dependent variable represents the expected classification performance. We
compute the dataset clustering indices and directly predict the expected
classification performance using the learned regressor for each candidate model
class to recommend a suitable model class for dataset classification. We
evaluate our model selection method through cross-validation with 60 publicly
available binary class datasets and show that our top3 model recommendation is
accurate for over 45 of 60 datasets. We also propose an end-to-end Automated ML
system for data classification based on our model selection method. We evaluate
our end-to-end system against popular commercial and noncommercial Automated ML
systems using a different collection of 25 public domain binary class datasets.
We show that the proposed system outperforms other methods with an excellent
average rank of 1.68.
