Precision and Recall are the two building blocks of the F1 score. The goal of the F1 score is tocombine the precision and recall metrics into a single metric.At the same time, the F1 score has been designed towork well on imbalanced data. F1 score formula The F1 score is defined as t...
F1-score, the harmonic mean of precision and recall, offers a balanced evaluation, especially important in cases of class imbalance within the dataset. Both metrics are standard in classification tasks and together give a comprehensive view of the model's capability to discern sentiments in ...
Up to Speed on Deep Learning: September, Part 2 and October, Part 1 My playlist – Top YouTube Videos on Machine HALearning, Neural Network & Deep Learning Designing Machine Learning Models: A Tale of Precision and Recall Deep Learning Study Caffe, TensorFlow The Deep Learning Playbook A Br...
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Learning Rate: Controls how fast the model learns. Model Training Issues: Overfitting: Too much training on the same data, causing the model to be overly specific. Solution: Use more diverse data during training. Underfitting: The model doesn’t learn enough patterns from the data. ...
Up to Speed on Deep Learning: September, Part 2 and October, Part 1 My playlist – Top YouTube Videos on Machine HALearning, Neural Network & Deep Learning Designing Machine Learning Models: A Tale of Precision and Recall Deep Learning Study Caffe, TensorFlow The Deep Learning Playbook A ...
Up to Speed on Deep Learning: September, Part 2 and October, Part 1 My playlist – Top YouTube Videos on Machine HALearning, Neural Network & Deep Learning Designing Machine Learning Models: A Tale of Precision and Recall Deep Learning Study Caffe, TensorFlow The Deep Learning Playbook A B...