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1
new-york-city-taxi-fare-prediction - Page11
iBooker it-ebooks
Kaggle Kernel
deprecationwarning
conda
opt
python3.6
packages
sklearn
deprecated
fixed
min_samples_leaf
tree.py
nodes
max_depth
extra
pruned
pruning
roots
src
updater_prune.cc
workspace
rmse
fare_amount
import
distance
dropoff_longitude
dropoff_latitude
pickup_longitude
pickup_latitude
pickup_datetime
df_train
passenger_count
axis
key
hour
false
1.000000e
input
mean_squared_error
data_train
row
0.000000e
float64
error
count
train_data
features
manhattan
columns
jfk
n_estimators
rows
Bahasa:
english
Fail:
EPUB, 2.58 MB
Tag anda:
0
/
0
english
2
Hands-On Gradient Boosting with XGBoost and scikit-learn: Perform accessible Python machine learning and extreme gradient boosting with Python
PACKT Publishing LTD
Corey Wade
xgboost
columns
trees
hyperparameters
boosting
column
models
values
random
dataset
import
gradient
max_depth
n_estimators
output
params
decision
scikit
accuracy
regression
predictions
scores
rows
function
kaggle
exoplanet
random_state
rmse
y_pred
method
forests
target
split
xgbclassifier
learning_rate
figure
hyperparameter
cross_val_score
error
scoring
range
recall
linear
dart
y_test
expected
classification
grid_search
learners
tuning
Tahun:
2020
Bahasa:
english
Fail:
EPUB, 6.01 MB
Tag anda:
0
/
0
english, 2020
3
Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping
MDPI
Misganaw Abebe
,
Yongwoo Shin
,
Yoojeong Noh
,
Sangbong Lee
,
Inwon Lee
ship
features
speed
models
wave
prediction
regression
current
dataset
total
feature
sci
appl
method
values
weather
accuracy
etr
hyperparameters
dtr
figure
resistance
ŷi
hyperparameter
obtained
optimization
rmse
trees
correlation
period
crossref
optimal
rfr
height
node
shipping
ships
gbr
shown
split
boosting
surface
computational
methodology
methods
n_estimators
observed
vessel
xgbr
addition
Tahun:
2020
Bahasa:
english
Fail:
PDF, 1.98 MB
Tag anda:
0
/
0
english, 2020
1
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