不同输入尺寸的神经网络选型及NBA球员梦幻得分预测回归模型咨询

Hey there, let's tackle your two questions with practical, actionable advice—no jargon overload, promise!

The right network depends entirely on the structure and length of your input data:

• Fixed-size structured data (like tabular data with a set number of features): Go with a Multi-Layer Perceptron (MLP). It’s perfect for mapping fixed-length feature vectors to your output, whether it’s regression or classification. For simpler cases, a generalized linear model (GLM) can also work as a strong baseline.
• Variable-length sequence data (like your player career score arrays): This is where Recurrent Neural Networks (RNNs) shine—specifically LSTMs or GRUs. They’re built to capture time-dependent trends, like a player’s rising or falling performance over seasons. Alternatively, Transformer Encoders are even better for longer sequences; their self-attention mechanism lets the model focus on the most important seasons (like recent form or peak years) regardless of sequence length.
• 2D grid/image data (if you ever wanted to use game footage, for example): Convolutional Neural Networks (CNNs) are non-negotiable. They extract spatial features (like movement patterns or shot selection) using convolution kernels, which is way more efficient than MLPs for this type of data.
• High-dimensional sparse data (like player transaction history or fan engagement stats): Factorization Machines (FM) or DeepFM models outperform standard MLPs here—they’re designed to model interactions between sparse features that regular networks might miss.

Your task is a time-series regression problem, and since you’re working with variable-length career score sequences, here’s a step-by-step plan:

1. Prep your data first #

• Standardize sequence lengths: Either pad shorter sequences with a placeholder (like 0 or the player’s average score) to match the longest sequence, or use sliding windows (e.g., take the last 5 seasons to predict the next 3). Don’t shuffle your data randomly—split it chronologically to mimic real-world prediction (you wouldn’t use future data to predict the past!).
• Normalize scores: Scale your data (e.g., using StandardScaler or MinMaxScaler) so values fall in a consistent range—this helps the model train faster and avoids bias toward higher-scoring players.
• Add extra features (if you can): Historical scores are great, but adding age, position, team changes, injury history, or even league-wide scoring trends will make your predictions way more accurate.

2. Choose your model #

I’d recommend starting with these two options:

• LSTM/GRU: Great for capturing long-term trends (like a player’s career arc). Here’s a quick Keras example:

  from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import LSTM, Dense, Dropout
  
  model = Sequential()
  # Input shape: (variable sequence length, 1 feature (score))
  model.add(LSTM(64, return_sequences=False, input_shape=(None, 1)))
  model.add(Dropout(0.2))  # Prevent overfitting
  model.add(Dense(32, activation='relu'))
  model.add(Dense(3))  # Output: next 3 seasons' scores
  model.compile(optimizer='adam', loss='mse')  # MSE is standard for regression
  

• Transformer Encoder: Better if you have longer sequences and want the model to prioritize key seasons. Here’s a simplified version:

  from tensorflow.keras.layers import Input, Dense, MultiHeadAttention, LayerNormalization, Dropout
  from tensorflow.keras.models import Model
  
  def transformer_block(inputs, head_size, num_heads, ff_dim):
      # Multi-head attention layer
      x = MultiHeadAttention(key_dim=head_size, num_heads=num_heads)(inputs, inputs)
      x = Dropout(0.2)(x)
      x = LayerNormalization(epsilon=1e-6)(x + inputs)
      # Feed-forward network
      ff_x = Dense(ff_dim, activation='relu')(x)
      ff_x = Dense(inputs.shape[-1])(ff_x)
      ff_x = Dropout(0.2)(ff_x)
      return LayerNormalization(epsilon=1e-6)(x + ff_x)
  
  inputs = Input(shape=(None, 1))
  x = transformer_block(inputs, head_size=32, num_heads=4, ff_dim=64)
  x = Dense(64, activation='relu')(x)
  outputs = Dense(3)(x)
  
  model = Model(inputs, outputs)
  model.compile(optimizer='adam', loss='mse')
  

3. Train and tune your model #

• Use early stopping: Stop training when the validation loss stops improving to avoid overfitting.
• Experiment with hyperparameters: Tweak the number of LSTM units, transformer heads, or learning rate to see what works best for your data.
• Evaluate with regression metrics: Track Mean Squared Error (MSE), Mean Absolute Error (MAE), and R² Score to measure how close your predictions are to real values.

内容的提问来源于stack exchange，提问作者Mike Griffin