# Performance Predictions of Sci-Fi Films via Machine Learning
## 5 Discussion  
This paper provides a modified KNN -ML approach as an alternate approach to open-space risk reduction that employs k-nearest neighbor approaches to discover discriminative characteristics. In order to study the role of k, we conduct contrastive learning with a variety of k values (within a given range) and compare the model's performance in detecting out-of-domain (OOD) intents across the board (while other hyper-parameters remain constant). The results demonstrate that as the value of k is increased, the model's performance (cosine-based) decreases across datasets. Indeed, this was to happen as k increased, and the possibility that an OOD sample would be incorrectly identified as in-domain (IND) decreased because of the decreased proportion of open space at the outset of the process. Later, as the IND semantic space shrank, a more significant percentage of intra-OOD samples were labeled as IND, and this trend tends to stabilize. Adopting this strategy also shows that the proposed method in this work is better than other methods for lowering the open-space risk, which is the risk that all IND intents from the same class will collide. These modifications to the KNN algorithm had a significant impact on the achieved results. Figure 8: Accuracy, precision, and recall for various machine learning algorithms. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Accuracy** & **Precision** & **Recall** \\ \hline Multinomial & 0.29 & 0.23 & 0.29 \\ Regression & & & \\ Fine KNN & 0.93 & 0.91 & 0.91 \\ Medium KNN & 0.90 & 0.89 & 0.88 \\ Weighted KNN & 0.93 & 0.93 & 0.92 \\ Bi-Directional LSTM & 0.79 & 0.75 & 0.77 \\ LSTM & 0.78 & 0.80 & 0.78 \\ GRU & 0.77 & 0.79 & 0.77 \\ Logistic regression & 0.89 & 0.88 & 0.88 \\ Naive Bayes & 0.81 & 0.80 & 0.81 \\ Decision tree & 0.87 & 0.86 & 0.87 \\ Gradient Boosting & 0.87 & 0.86 & 0.86 \\ Random Forest & 0.85 & 0.84 & 0.84 \\ SVM linear kernel & 0.73 & 0.72 & 0.71 \\ SVM RBF kernel & 0.75 & 0.74 & 0.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracy, precision, and recall for various machine learning algorithms. As listed in [18; 19; 20; 21; 22; 23; 24; 25], the best achieved accuracy was very low compared to the tabulated results in this work. In [20], multiple algorithms were used for analysis; among these, the one with the highest prediction accuracy compared to previous studies was all random forest. The preliminary investigation revealed that the number of voting users, number of critics for reviews, number of Facebook likes, duration of the film, and gross receipts all significantly impact the IMDb score. Drama and biopic films were the best genre films [20]. Having mentioned that, it is evident that their approach depended not only on historical data, but also on future data, which limits its applicability. Furthermore, their results were below the tabulated results of this work. In [21], they analyzed some of the variables that can affect a film's success and how these variables affect a film's success over time. In their work, they came up with a model that considers many factors, gives each one a weight, and predicts whether a movie will do well or not based on how important each factor is. Their work could be enhanced so that their predictions are more accurate. However, more crucial indicators were required to evaluate their methodology and provide accurate statistics. [PERSON], et al. trained an IMDb-extracted random forest algorithm to predict the success of a film. In addition, the Naive Bayer model was trained using YouTube user evaluations to estimate the rating of a movie [22]. The accuracy of the models was evaluated based on their performance on real-world datasets. Two results were reached: the rating of a new film cannot be anticipated in advance using the comments on its trailers and teasers on YouTube, while the success of a new film may be predicted in advance using online data or characteristics.