Following the success of predicting 6 out of 6 for the Oscars last year, we have the bar set high for using Machine Learning to predict the 2019 Oscars winners. This year, however, the results are not as obvious. For some of the top categories, our projected results show ties for who gets to take home the coveted gold statuette. Nevertheless, we are excited to share our predictions and see how the Academy Awards pan out this Sunday!
Once again, we apply the standard Machine Learning workflow of collecting and preparing a dataset, building and evaluating models, to ultimately make predictions. Using the 1-click OptiML on BigML to find the best models, we easily process more than 100 variables and determine patterns based on the movies that won in the past and to make well-informed estimates for this year’s nominees.
Earlier this week, we published our Movies dataset and encouraged users to build their own models to predict the 2019 Oscars. Machine Learning models typically improve with more data instances so we are keeping all the previous data and features we had brought together for previous year’s predictions, and we added data from 2018, all of which amounts to a total of 1,235 movies from 2000 to 2018, where each film has 100+ features including:
- Film characteristics such as duration, budget, and genre.
- Film evaluation measures in IMDB like votes, rating, and Metascore.
- This year’s nominations and winners for 20 key industry awards including Golden Globes, BAFTA, Screen Actors Guild, and Critics Choice.
In addition to using deepnets as we did for the 2018 predictions, this year we also utilize OptiML, the optimization process on BigML that automatically finds the best supervised model, along with Fusions, which combines multiple supervised models for improved performance. So for each award category, we trained two separate model types to see how the predictions would compare and which method would give the best results.
For the new workflow we tried this year, we first built the OptiML, which returns a list of top performing models including deepnets, ensembles, logistic regressions, and decision trees. This powerful method saves you the difficult and time-consuming work of hand-tuning multiple supervised algorithms. With truly the click of a button, we can automatically build and evaluate hundreds of models. As you can see in the screenshot below, after a mere 16 minutes, our OptiML has already evaluated 126 models.
After our OptiML was finished, we created a Fusion of the top models and then made a Batch Prediction. As an example of the insights that can be gleaned from this process, our models determine which are the most important fields to predict the “Best Picture” award, as shown below in the field importance report for our Fusion model). The “Critics Choice won categories” appears to be the strongest indicator, contributing 27% to our model’s predictions.
Now comes the fun part. Let’s predict the 2019 winners! For each category, we predict the winner and the scores for the rest of the nominees.
In the battle for the Best Picture, our models went back and forth between The Favourite and Roma. The deepnet predicted The Favourite will be the favorite (no pun intended!) with a score of 37. The OptiML + Fusion models predicted Roma would be the big winner, but with a lower probability score of 24, so we stuck with the deepnet predictions for this category.
For Best Director, our models are much more confident. Alfonso Cuarón, director of Roma, is the likely winner with a score of 70 and the other nominees trail far behind.
For Best Actress, Glenn Close in The Wife is the leading lady with a score of 93.
The strongest prediction from all our models was a score of 96 for the Best Actor award going to Rami Malek for this stellar performance in Bohemian Rhapsody. Rock on, Rami!
Our models weren’t as convinced for the Best Supporting Actress category. Emma Stone is our pick with a humble score of 23. Even the machines can’t figure it out all the time.
Bouncing back, our models are feeling pretty good about Mahershala Ali in the Green Book winning Best Supporting Actor with a score of 64.
First Reformed seems to be the best bet for Best Original Screenplay with a score of 46, followed by close ties between Roma (17) and The Favourite (16) once again.
And last but not least, our models give the gold to BlacKkKlansman for Best Original Screenplay.
This concludes our 2019 Oscars predictions. After curling up on the couch with popcorn, we’ll be on the edge of our seats as we find out how our Machine Learning models performed while watching the awards show live this Sunday, February 24th. Check back on Monday when we’ll share our results for this year’s predictions…fingers crossed!