• Accuracy:
0.8042693926638604
Our model outputs are shown in Figures 4a and 4b.
(a) Enlarged Graph (0-5000 seconds)
(b) Graph for all Data Points
Figure 4: Comparison of Graphs
Analyzing the two graphs and the slope of the logistic curve, w, which is very small (almost close to 0), it seems the log odds doesn’t depend on X. Equivalently, estimated P(y=1 — x) doesn’t vary with x. Hence, day/night is not being related to the length of encounter. 3.4 Support Vector Machine 1. Studying the Relation Between Latitude, Longitude, and Day/Night Prediction • The dataset was split into a training set (80 % of the data), validation set (10 % of the data), and test set (10 % of the data). • SVM was trained with RBF kernel for training. • Model performance metrics: Accuracy: 0.804 Root Mean Squared Error (RMSE): 0.885
• Decision boundary plots
(a) SVM with Decision Boundary (RBF Kernel) for Training Data
(b) SVM with Decision Boundary (RBF Kernel) for Testing Data
(c) SVM with Decision Boundary (RBF Kernel) for both
Figure 5: Support Vector Machine (SVM) Results
RMSE: 0.885. Predictions deviate by 0.885 units. SVM accuracy: 0.804, correctly predicts 80 . 41 Graphs suggest day/night, not solely location-dependent.
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