Data Quality and Preprocessing
- Inspect your training data for imbalances or noise. Large discrepancies or corrupted data can significantly affect model accuracy.
- Ensure appropriate data normalization or standardization. Inconsistent features can skew predictions.
- Consider augmenting data artificially if the dataset is too small to encompass the variety needed in inputs.
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaled_features = scaler.fit_transform(features)
Model Complexity and Architecture
- Check if your model is too complex or too simple for your task. An overly simple model may underfit, while an overly complex model may overfit.
- Experiment with different architectures and layer sizes. In neural networks, using dropout layers can help with model generalization.
from tensorflow.keras.layers import Dropout
model.add(Dropout(0.5))
Hyperparameter Tuning
- Adjust learning rates, batch sizes, and other hyperparameters. Too high or too low learning rates can lead to suboptimal convergence.
- Utilize grid search or random search to find the best parameter combinations for your model.
from tensorflow.keras.optimizers import Adam
optimizer = Adam(learning_rate=0.001)
Training Process
- Monitor for signs of overfitting, such as the validation loss increasing while training loss decreases. Implement early stopping to mitigate this.
- Ensure the training is not halted prematurely due to resource constraints or errors during the process.
from tensorflow.keras.callbacks import EarlyStopping
early_stopping = EarlyStopping(monitor='val_loss', patience=5)
Evaluation Metrics
- Review the evaluation metrics you are using – accuracy alone may not be the best metric depending on the nature of the problem, such as class imbalance.
- Consider using F1 Score, Precision, Recall, or ROC-AUC for more balanced information about the model's performance.
- Cross-validate your model accuracy to ensure that it generalizes well to unseen data.
from sklearn.model_selection import cross_val_score
cross_val_score(model, X, y, cv=5)
Hardware and Computational Resources
- Verify that your computational resources are sufficient for training. Insufficient memory or processing power can throttle the capability to achieve high accuracy.
- Consider using cloud platforms with GPUs or TPUs to accelerate training times and possibly improve the quality of the models built.
