220 is a NVIDIA networking product designed for enterprise and data center networking.
ODSC is a NVIDIA networking product designed for AI clusters, data centers.
AI is a networking product designed for AI clusters, enterprise networks.
This product category is a NVIDIA networking product designed for enterprise and data center networking.
10GB is a networking product designed for enterprise and data center networking. Key specifications include 10GB.
pandas is a networking product designed for enterprise and data center networking.
Shyamal is a networking product designed for enterprise and data center networking. It supports NDR.
: Shyamal Shah
This product category is a NVIDIA networking product designed for enterprise and data center networking.
# Calculate statistics from training data
base_median = train_df[base_feature].median()
Q1 = train_df[base_feature].quantile(0.25)
Q3 = train_df[base_feature].quantile(0.75)
IQR = Q3 - Q1
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR
# Process base feature
df_processed['magical'] = df['magical'].fillna(base_median).clip(lower_bound, upper_bound)
106 is a networking product designed for enterprise and data center networking.
# Calculate robust target encodings for high-cardinality categorical variables
cat_encodings = {}
global_mean = train_df['y'].mean()
for col in ['trickortreat', 'kingofhalloween']:
# Group by category and calculate stats
cat_stats = (train_df.groupby(col)['y']
.agg(['mean', 'count'])
.reset_index())
# Only keep categories that appear more than once
frequent_cats = cat_stats[cat_stats['count'] > 1]
# Strong smoothing factor due to high cardinality
smoothing = 100
# Calculate smoothed means with stronger regularization
frequent_cats['encoded'] = (
(frequent_cats['count'] * frequent_cats['mean'] + smoothing * global_mean) /
(frequent_cats['count'] + smoothing)
)
# Create dictionary only for frequent categories
cat_encodings[col] = dict(zip(frequent_cats[col], frequent_cats['encoded']))
# Process categorical features
for col in ['trickortreat', 'kingofhalloween']:
# Map categories to encodings, with special handling for rare/unseen categories
df_processed[f'{col}_encoded'] = (
df[col].map(cat_encodings[col])
.fillna(global_mean) # Use global mean for rare/unseen categories
)
Microsoft is a networking product designed for enterprise and data center networking.
47 is a networking product designed for enterprise and data center networking.
: Feifan Liu and Himalaya Dua and Sara Zare
cuDF is a networking product designed for enterprise and data center networking.
imputation is a networking product designed for enterprise and data center networking.
train_df = df.copy()
# train_df = sample_20_df.copy()
categorical_cols = train_df.select_dtypes(include=['object', 'category']).columns.tolist()
numerical_cols = train_df.select_dtypes(include=['number']).columns.tolist()
num_col_only_minus_one = [col for col in numerical_cols if (train_df[col]
> 0 and (train_df[col]
train_df[categorical_cols] = train_df[categorical_cols].astype('category')
train_df[num_col_only_minus_one]=train_df[num_col_only_minus_one].replace(-1, np.nan)
test_df[categorical_cols] = test_df[categorical_cols].astype('category')
test_df[num_col_only_minus_one]=test_df[num_col_only_minus_one].replace(-1, np.nan)
XGBoost is a networking product designed for enterprise and data center networking.
#baseline parameters
xgb_regressor = xgb.XGBRegressor(objective='reg:squarederror', eval_metric = 'rmse',
max_depth= 5, n_estimators=500, random_state=42, device='cuda', enable_categorical=True)
: Lorenzo Mondragon
RAPIDS is a networking product designed for enterprise and data center networking.
GPU is a networking product designed for enterprise and data center networking.
Unknown is a networking product designed for enterprise and data center networking.
UInt32 is a networking product designed for enterprise and data center networking.
Polars is a networking product designed for enterprise and data center networking.
# 1. Handle missing values
numeric_cols = train_data.select(cs.numeric()).columns
categorical_cols = [
col for col in train_data.columns
if col not in numeric_cols and col not in ['id', 'y']
]
# Fill missing values
df = train_data.with_columns([
# Fill numeric columns with mean
*[
pl.col(col).fill_null(pl.col(col).mean()).alias(col)
for col in numeric_cols
],
# Fill categorical columns with 'Unknown'
*[
pl.col(col).fill_null("Unknown").alias(col)
for col in categorical_cols
]
])
Polars is a networking product designed for enterprise and data center networking.
GPU is a networking product designed for enterprise and data center networking.
RAPIDS is a networking product designed for enterprise and data center networking.
This product is a networking product designed for enterprise and data center networking.
Polars is a networking product designed for enterprise and data center networking.
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