<jupyter_start><jupyter_text>Predicciones en set no visto: Validación<jupyter_code>validation_df = pd.read_csv("sports_Scoring.csv")
validation_df.head()
# Hacemos las transformaciones necesarias -> hacer que Capacidad_decision contenga el rango correcto de valores
validation_df = validation_df[(validation_df['CapacidadDecision'] >= 3) &
 (validation_df['CapacidadDecision'] <= 100)]
validation_df.describe()
validation_df.columns
y_pred = lda.predict(validation_df.to_numpy())
print(y_pred)<jupyter_output>[0 2 1 ... 1 0 1]
{'fraction_non_alphanumeric': 0.11320754716981132, 'fraction_numerical': 0.018867924528301886, 'mean_word_length': 7.704918032786885, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 6, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '343394', 'license': 'MIT', 'n_tokens_mistral': 198, 'n_tokens_neox': 184, 'n_words': 36, 'path': 'Algoritmos Lineales/PD5Ej2.ipynb', 'repo_name': 'JoseVilaseca/josevilaseca.github.io', 'size': '530'}