Overfitting

Overfitting refers to the phenomenon where a model performs excellently on training data but its performance drops significantly on unseen test data.

The quality and diversity of training data directly affects the reliability of model outputs, and training data containing large amounts of low-quality, contradictory, or false information can cause models to learn incorrect patterns.

During the post-training phase, an excessively low loss may correspond to overfitting, where the model memorizes the training data and loses generalization ability.

If samples are too easy the model has nothing to learn; if they are too difficult, noisy learning signals may interfere with training; the most effective learning occurs in the region slightly above current capability but still within reach.

Cross-validation should be used rather than a single train/test split to evaluate model performance more reliably.

Normalization parameters must be calculated only from the training set and then applied to the validation and test sets to avoid data leakage.

识别过拟合的经典方法是比较训练集AUC和验证集AUC——如果训练集AUC接近1.0而验证集AUC明显偏低，就是过拟合的典型信号

神经网络层数越多、神经元越多，模型的表达能力越强，但也越容易出现过拟合

Overfitting refers to a model performing well on training data but poorly on new data.

Common strategies for addressing overfitting on small datasets include data augmentation, regularization techniques (such as Dropout and weight decay), early stopping, and controlling model capacity.

Signals for identifying overfitting include: the more strategy parameters the better; backtest results being extremely sensitive to small parameter changes; strategy performance dropping off a cliff in out-of-sample testing; and too few trades leading to insufficient statistical significance.

Common signals of overfitting include: the number of strategy parameters far exceeding the explicable trading logic; an abnormally smooth equity curve within a single period; and extreme sensitivity to minor parameter adjustments.

A successful backtest does not prove future profitability; a strategy may be over-optimized to fit historical data, that is, overfitted.

The low consistency between the feedback score and the held-out score is essentially a concrete manifestation of overfitting and the generalization gap in the scenario of agent self-improvement.

In transfer learning practice, the smaller the amount of data for the new task, the more layers should be frozen to prevent overfitting; the learning rate should be much smaller than when training from scratch; if the new data differs greatly in distribution from the pretraining data, more layers often need to be fine-tuned.

Overfitting means the model performs perfectly on training data but performs poorly on new data, equivalent to rote memorization rather than learning the underlying patterns.

In evaluation set design, a holdout set refers to the portion of data deliberately kept out of the optimization process and used only for final validation, to detect overfitting, provided that the scorer definition reflects the good outputs you truly care about.

The typical signal of overfitting is that training set accuracy keeps rising while validation set accuracy stagnates or even declines.

普通回测允许开发者反复调参直到曲线好看，极易产生过拟合，策略只是记住了历史噪声而非真实规律。

Convexity implies the existence of a unique optimal heavy-tail degree, and this optimal value can empirically predict the occurrence of overfitting on large-scale DNNs.