为什么MLP分类器算法会给我一些极端值？

Question

我正在尝试使用MLP分类器算法，想要分析过去666次从36个不同的 urn（每个 urn 里有10个编号为0到9的球）中抽取一个球的结果。但是，当我让算法给我下次抽球的每个球的期望概率时，它给出的结果却是极端值。以下是我的代码：

import numpy as np
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler

# Assuming your data is stored in the 'results' list
# Each sublist represents the past results for an urn
results = [
    [3, 2, 7, ..., 6, 9],  # Urn 1
    [5, 2, ..., 0],       # Urn 2
    # ... (other urns)
    [4, 2, 7, ..., 1, 1]  # Urn 36
]

# Encode the past results as binary vectors
encoder = OneHotEncoder(categories=[range(10)] * 666, sparse=False)
X = np.vstack([encoder.fit_transform([urn]) for urn in results])

# Use the last ball drawn in each urn as the label
labels = np.array([urn[-1] for urn in results])

# Normalize the features
scaler = StandardScaler()
X_normalized = scaler.fit_transform(X)

# Initialize the MLP classifier with increased complexity
mlp = MLPClassifier(hidden_layer_sizes=(100, 100, 100), max_iter=1000, random_state=42)

# Train the model
mlp.fit(X_normalized, labels)

# Predict probabilities for each ball in the next drawing for all urns
for urn_idx, urn_results in enumerate(results):
    next_drawing_probs = mlp.predict_proba(scaler.transform(encoder.transform([urn_results])))
    print(f"Urn {urn_idx + 1} probabilities:")
    for ball, prob in enumerate(next_drawing_probs):
        print(f"  Ball {ball}: {prob:.4f}")


#And here is the output for the last 2 urns (which are like the output for all the other 34 urns):

Urn 35 probabilities:
  Ball 0: 0.0000
  Ball 1: 0.0000
  Ball 2: 0.0000
  Ball 3: 0.0000
  Ball 4: 0.0001
  Ball 5: 0.9999
  Ball 6: 0.0000
  Ball 7: 0.0000
  Ball 8: 0.0000
  Ball 9: 0.0000
Urn 36 probabilities:
  Ball 0: 0.0000
  Ball 1: 0.0000
  Ball 2: 0.0000
  Ball 3: 0.0000
  Ball 4: 0.0000
  Ball 5: 0.0000
  Ball 6: 0.0000
  Ball 7: 0.0000
  Ball 8: 0.0000
  Ball 9: 0.9999

Process finished with exit code 0

我尝试增加层数（最多到1000层），但结果还是一样。我希望每个球的概率能接近0.1（最低可能是0.075，最高可能是0.125）。