前馈神经网络回归预测值范围受限，寻求改进方案

Hey there! It’s super common to run into this issue when working on regression tasks with extreme target ranges—let’s break down the most likely fixes step by step:

• Check your output layer activation function first
  This is probably the biggest culprit. If you used something like sigmoid (outputs 0-1) or tanh (outputs -1 to 1) for your output layer, that’s directly limiting your predictions to those narrow ranges. For regression, your output layer should use a linear activation (no activation function at all, or explicitly set to linear). This lets the model output values across the full range of your target variable.

• Normalize/standardize your target variable
  Your target has a huge range (-1000 to 125), which can make it hard for the model to learn the scale. Try scaling your target values to a manageable range (like 0-1 with MinMaxScaler or mean 0/variance 1 with StandardScaler) before training. After making predictions, simply inverse-transform the scaled outputs back to the original range to get valid predictions. Here’s a quick code snippet example:

  from sklearn.preprocessing import MinMaxScaler
  
  # Fit scaler on training targets
  scaler = MinMaxScaler()
  y_train_scaled = scaler.fit_transform(y_train.reshape(-1, 1))
  
  # Train your model with y_train_scaled
  # ...
  
  # Predict and inverse transform
  y_pred_scaled = model.predict(X_test)
  y_pred = scaler.inverse_transform(y_pred_scaled)
  

• Adjust your model’s capacity
  If your network is too small (too few layers or neurons), it might not have enough expressive power to capture the wide range of your target. Try:

  • Adding more hidden layers (e.g., go from 1 to 2-3 hidden layers)
  • Increasing the number of neurons per hidden layer (e.g., from 16 to 32 or 64)
  • Using more flexible activation functions in hidden layers, like LeakyReLU or Swish, instead of plain ReLU—these help avoid "dead neurons" that limit learning.

• Tweak your training setup

  • Loss function: For regression, MSE is standard, but if you have outliers in your data, MAE (Mean Absolute Error) might be more robust and help the model focus on capturing the full range instead of just minimizing large errors from outliers.
  • Optimizer & learning rate: Adam is usually a better starting point than SGD for most tasks, as it adapts the learning rate automatically. If you’re using SGD, make sure your learning rate isn’t too low (which slows convergence) or too high (which causes unstable training). You can also try learning rate scheduling to reduce the rate as training progresses.
  • Training epochs: If your training loss is still decreasing after your current number of epochs, the model hasn’t finished learning—let it train longer. Just keep an eye on validation loss to avoid overfitting.

• Don’t forget input feature scaling
  If your input features have wildly different scales, the model might prioritize learning from the largest-scale features and ignore others that could help it predict the full target range. Normalize or standardize all input features to the same scale before training.

Start with the first two fixes (output activation and target scaling)—they’re the quickest wins and solve this problem most of the time. Then iterate on the others if needed!

内容的提问来源于stack exchange，提问作者Navin Kumar