kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
7,677,885 | model = create_model(input_shape=(IMG_WIDTH, IMG_HEIGHT, CHANNEL), n_out=NUM_CLASSES)
for layer in model.layers:
layer.trainable = False
for i in range(-7, 0):
model.layers[i].trainable = True
metric_list = ["accuracy"]
optimizer = optimizers.Adam(lr=WARMUP_LEARNING_RATE)
model.compile(optimizer=optimizer, loss="cate... | train_data['HasCabin'] = train_data['Cabin'].notnull().astype('int')
test_data['HasCabin'] = test_data['Cabin'].notnull().astype('int' ) | Titanic - Machine Learning from Disaster |
7,677,885 | datagen = ImageDataGenerator(
rescale=1./255.,
validation_split=0.25)
train_generator = datagen.flow_from_dataframe(
dataframe=TRAIN_DF,
directory=TRAIN_DIR,
x_col=X_COL,
y_col=Y_COL,
subset="training",
batch_size=BATCH_SIZE,
seed=SEED,
zoom_range=0.2,
horizontal_flip=True,
class_mode="categorical",
preprocessing_fu... | train_data.drop('Cabin', axis=1, inplace=True)
test_data.drop('Cabin', axis=1, inplace=True ) | Titanic - Machine Learning from Disaster |
7,677,885 | STEP_SIZE_TRAIN=train_generator.n//train_generator.batch_size
STEP_SIZE_VALID=valid_generator.n//valid_generator.batch_size
STEP_SIZE_TEST=test_generator.n//test_generator.batch_size
print(STEP_SIZE_TRAIN)
print(STEP_SIZE_VALID)
print(STEP_SIZE_TEST )<set_options> | train_data['Embarked'].value_counts() | Titanic - Machine Learning from Disaster |
7,677,885 | gc.collect()<train_model> | full_data = pd.concat([train_data.drop('Survived', axis=1), test_data], ignore_index=True ) | Titanic - Machine Learning from Disaster |
7,677,885 | history_warmup = model.fit_generator(generator=train_generator,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=valid_generator,
validation_steps=STEP_SIZE_VALID,
epochs=WARMUP_EPOCHS,
verbose=1 ).history<set_options> | full_data[['Age', 'Pclass', 'Sex', 'Embarked']].groupby(['Pclass', 'Sex', 'Embarked'])['Age'].mean() | Titanic - Machine Learning from Disaster |
7,677,885 | gc.collect()<choose_model_class> | train_data[['Age', 'Pclass', 'Sex', 'Embarked']].groupby(['Pclass', 'Sex', 'Embarked'])['Age'].mean() | Titanic - Machine Learning from Disaster |
7,677,885 | for layer in model.layers:
layer.trainable = True
es = EarlyStopping(monitor='val_loss',
mode='min',
patience=ES_PATIENCE,
restore_best_weights=True,
verbose=1)
rlrop = ReduceLROnPlateau(monitor='val_loss',
mode='min',
patience=RLROP_PATIENCE,
factor=DECAY_DROP,
min_lr=1e-6,
verbose=1)
model_checkpoint = ModelCheckpo... | Titanic - Machine Learning from Disaster | |
7,677,885 | collected = gc.collect()
print("Garbage collector: collected","%d objects." % collected )<train_model> | def get_age(element):
age = element[0]
pclass = element[1]
sex = element[2]
embarked = element[3]
if(pd.isnull(age)) :
temp_data = full_data[(full_data['Pclass'] == pclass)&(full_data['Sex'] == sex)&(full_data['Embarked'] == embarked)]
mean_age = temp_data['Age'].mean()
return mean_age
return age
train_data['Age'] = tr... | Titanic - Machine Learning from Disaster |
7,677,885 | history_finetunning = model.fit_generator(generator=train_generator,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=valid_generator,
validation_steps=STEP_SIZE_VALID,
epochs=EPOCHS_OLD_DATA,
callbacks=callback_list,
verbose=1 ).history<set_options> | sex = pd.get_dummies(train_data['Sex'], prefix='Sex', drop_first=True)
embarked = pd.get_dummies(train_data['Embarked'], prefix='Embarked', drop_first=True)
title = pd.get_dummies(train_data['Title'], prefix='Title', drop_first=True)
train_data.drop(['Sex', 'Title', 'Embarked'], axis=1, inplace=True)
train_data = p... | Titanic - Machine Learning from Disaster |
7,677,885 | gc.collect()<load_pretrained> | sex = pd.get_dummies(test_data['Sex'], prefix='Sex', drop_first=True)
embarked = pd.get_dummies(test_data['Embarked'], prefix='Embarked', drop_first=True)
title = pd.get_dummies(test_data['Title'], prefix='Title', drop_first=True)
test_data.drop(['Sex', 'Title', 'Embarked'], axis=1, inplace=True)
test_data = pd.con... | Titanic - Machine Learning from Disaster |
7,677,885 | model = load_model('EfficientNetB5_Best_KV.h5' )<define_variables> | y_train = train_data['Survived']
X_train = train_data.drop(['Survived', 'Fare'], axis=1)
test_data = test_data.drop(['Fare'], axis=1)
| Titanic - Machine Learning from Disaster |
7,677,885 | if test_generator.n%BATCH_SIZE > 0:
PREDICTION_STEPS =(test_generator.n//BATCH_SIZE)+ 1
else:
PREDICTION_STEPS =(test_generator.n//BATCH_SIZE)
print(PREDICTION_STEPS )<define_variables> | kfold = StratifiedKFold(n_splits=5)
random_state = 13 | Titanic - Machine Learning from Disaster |
7,677,885 | print(test_generator.n)
print(test_generator.batch_size)
print(STEP_SIZE_TEST)
print(BATCH_SIZE )<predict_on_test> | random_forest_classifier = RandomForestClassifier(random_state=random_state)
cv_result = cross_val_score(random_forest_classifier, X_train, y_train, cv=kfold, scoring='accuracy')
print("CV result mean: ", cv_result.mean() , "CV result std: ", cv_result.std())
cv_result | Titanic - Machine Learning from Disaster |
7,677,885 | test_generator.reset()
preds = model.predict_generator(test_generator,
steps=PREDICTION_STEPS,
verbose=1)
predictions = [np.argmax(pred)for pred in preds]<set_options> | xgbClassifier = XGBClassifier(random_state=random_state)
cv_result = cross_val_score(xgbClassifier, X_train, y_train, cv=kfold, scoring='accuracy')
print("CV result mean: ", cv_result.mean() , "CV result std: ", cv_result.std())
cv_result | Titanic - Machine Learning from Disaster |
7,677,885 | gc.collect()<define_variables> | rf_param_grid = {'max_depth' :[1, 2, 3, 4, 5, 6],
'max_features' :[2, 4, 6, 8, 10],
'min_samples_split':[2, 4, 6, 8, 10],
'bootstrap' :[False, True],
'n_estimators' :[50, 100, 200, 500],
'criterion' :['gini']}
grid_search_random_forest_classifier = GridSearchCV(random_forest_classifier, param_grid=rf_param_grid, cv=kfo... | Titanic - Machine Learning from Disaster |
7,677,885 | filenames = test_generator.filenames
<save_to_csv> | grid_search_random_forest_classifier.best_params_ | Titanic - Machine Learning from Disaster |
7,677,885 | results = pd.DataFrame({'id_code':filenames, 'diagnosis':predictions})
results['id_code'] = results['id_code'].map(lambda x: str(x)[:-4])
results.astype({'diagnosis': 'int64'})
results.to_csv('submission.csv',index=False)
print(results.head(10))<set_options> | xgb_param_grid={'colsample_bylevel':[0.1, 0.9, 1],
'colsample_bytree' :[0.2, 0.8, 1],
'gamma' :[0.99, 9, 99],
'max_depth' :[2, 4, 6, 8, 10],
'min_child_weight' :[1, 2, 4, 6, 8, 10],
'n_estimators' :[10, 20, 50, 70, 100, 200, 500, 1000]}
grid_search_xgboost_classifier = GridSearchCV(xgbClassifier, param_grid=xgb_param_g... | Titanic - Machine Learning from Disaster |
7,677,885 | gc.collect()<import_modules> | grid_search_xgboost_classifier.best_params_ | Titanic - Machine Learning from Disaster |
7,677,885 | from fastai import *
from fastai.vision import *
import pandas as pd
import matplotlib.pyplot as plt
import pandas as pd
import os
import numpy as np
import pandas as pd
import glob
import matplotlib.pyplot as plt
import imagehash
import psutil
from PIL import Image
from joblib import Parallel, delayed
import matplotli... | x_tr, x_val, y_tr, y_val = train_test_split(X_train, y_train, test_size=0.3, random_state=random_state ) | Titanic - Machine Learning from Disaster |
7,677,885 | base_image_dir = os.path.join('.. ', 'input/aptos2019-blindness-detection/')
train_dir = os.path.join(base_image_dir,'train_images/')
df = pd.read_csv(os.path.join(base_image_dir, 'train.csv'))
df['path'] = df['id_code'].map(lambda x: os.path.join(train_dir,'{}.png'.format(x)))
df = df.drop(columns=['id_code'])
df ... | random_forest_classifier = RandomForestClassifier(n_estimators=200, max_depth=3, max_features=8, min_samples_split=2, bootstrap=True, random_state=random_state)
random_forest_classifier.fit(x_tr, y_tr)
predicted_test = random_forest_classifier.predict(x_val)
accuracy_random_forest = accuracy_score(y_val, predicted_t... | Titanic - Machine Learning from Disaster |
7,677,885 | bs = 32
sz=140<compute_test_metric> | xgbClassifier = XGBClassifier(max_depth=4,n_estimators=20,gamma=9,colsample_bylevel=0.9,random_state=random_state)
xgbClassifier.fit(x_tr, y_tr)
predicted_test = xgbClassifier.predict(x_val)
accuracy_xgb = accuracy_score(y_val, predicted_test)
f1_xgb = f1_score(y_val, predicted_test)
print("XGBooster scores: ", ac... | Titanic - Machine Learning from Disaster |
7,677,885 | def quadratic_kappa(y_hat, y):
return torch.tensor(cohen_kappa_score(torch.argmax(y_hat,1), y, weights='quadratic'),device='cuda:0' )<choose_model_class> | xgbClassifier.fit(X_train, y_train)
submission_prediction = xgbClassifier.predict(test_data)
submission = pd.DataFrame({
'PassengerId': test_data['PassengerId'],
'Survived': submission_prediction
})
submission_file_path = './titanic_submission.csv'
submission.to_csv(submission_file_path, index=False ) | Titanic - Machine Learning from Disaster |
7,705,538 | learn= cnn_learner(data, base_arch=models.vgg19_bn, metrics = [accuracy,quadratic_kappa] )<train_model> | train_data = pd.read_csv("/kaggle/input/titanic/train.csv")
train_data.head() | Titanic - Machine Learning from Disaster |
7,705,538 | learn.fit_one_cycle(7)
learn.lr_find()
learn.recorder.plot()<train_model> | test = pd.read_csv("/kaggle/input/titanic/test.csv")
test.head() | Titanic - Machine Learning from Disaster |
7,705,538 | learn.data = data =(
src.transform(get_transforms(tfms),size=255)
.databunch(bs=bs,num_workers=4)
.normalize()
)
learn.freeze()<train_model> | train_data = train_data.drop(columns = ['Cabin','Name','Ticket','PassengerId'] ) | Titanic - Machine Learning from Disaster |
7,705,538 | learn.unfreeze()
learn.fit_one_cycle(9,slice(1e-4,1e-3))<load_from_csv> | train_data['Age'].fillna(( train_data['Age'].mean()), inplace=True ) | Titanic - Machine Learning from Disaster |
7,705,538 | sample_df = pd.read_csv('.. /input/aptos2019-blindness-detection/sample_submission.csv')
learn.data.add_test(ImageList.from_df(sample_df,'.. /input/aptos2019-blindness-detection/',folder='test_images',suffix='.png'))
preds,y = learn.get_preds(DatasetType.Test )<save_to_csv> | train_data[["Parch", "Survived"]].groupby(['Parch'], as_index=False ).mean().sort_values(by='Survived', ascending=False)
train_data[["SibSp", "Survived"]].groupby(['SibSp'], as_index=False ).mean().sort_values(by='Survived', ascending=False ) | Titanic - Machine Learning from Disaster |
7,705,538 | sample_df.diagnosis = preds.argmax(1)
sample_df.head()
sample_df.to_csv('submission.csv',index=False )<set_options> | yf = train_data.Survived
base_features = ['Parch','SibSp','Age', 'Fare','Pclass']
Xf = train_data[base_features]
train_X, val_X, train_y, val_y = train_test_split(Xf, yf, random_state=1)
first_model = RandomForestRegressor(n_estimators=21, random_state=1 ).fit(train_X, train_y ) | Titanic - Machine Learning from Disaster |
7,705,538 | %reload_ext autoreload
%autoreload 2
%matplotlib inline
warnings.filterwarnings("ignore")
%matplotlib inline
<load_pretrained> | train_data['FamilySize'] = train_data['SibSp'] + train_data['Parch']
train_data[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False ).agg('mean' ) | Titanic - Machine Learning from Disaster |
7,705,538 | md_ef = EfficientNet.from_pretrained('efficientnet-b5', num_classes=1)
!mkdir models
!cp '.. /input/kaggle-public/abcdef.pth' 'models'<feature_engineering> | train_data['IsAlone'] = 0
train_data.loc[train_data['FamilySize'] == 0, 'IsAlone'] = 1
train_data[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index=False ).mean() | Titanic - Machine Learning from Disaster |
7,705,538 | bs = 64
tfms = get_transforms(do_flip=True,flip_vert=True )<choose_model_class> | train_data[["Fare", "Survived"]].groupby(['Survived'], as_index=False ).mean().sort_values(by='Survived', ascending=False)
train_data.groupby(['Sex','Survived'])[['Fare']].agg(['min','mean','max'] ) | Titanic - Machine Learning from Disaster |
7,705,538 | learn.load('abcdef');
opt = OptimizedRounder()<predict_on_test> | y2 = train_data.Survived
base_features2 = ['Parch','SibSp','Age', 'Fare','Pclass','Age*Class','FamilySize','IsAlone']
X2 = train_data[base_features2]
train_X2, val_X2, train_y2, val_y2 = train_test_split(X2, y2, random_state=1)
second_model = RandomForestRegressor(n_estimators=21, random_state=1 ).fit(train_X2, train_... | Titanic - Machine Learning from Disaster |
7,705,538 | preds0,y = learn.get_preds(DatasetType.Test )<compute_test_metric> | dummies_Sex = pd.get_dummies(train_data.Sex)
dummies_Embarked = pd.get_dummies(train_data.Embarked)
train_ready = pd.concat([train_data, dummies_Sex,dummies_Embarked], axis=1)
train_ready.head()
train_ready = train_ready.drop(columns = ['Sex','Embarked'])
train_ready.info() | Titanic - Machine Learning from Disaster |
7,705,538 | preds =(preds0 + preds1 + preds2 + preds3 + preds4)/5<save_to_csv> | train_ready = train_ready.drop(columns = ['Age*Class'])
train_ready = train_ready.drop(columns = ['FamilySize'] ) | Titanic - Machine Learning from Disaster |
7,705,538 | tst_pred = opt.predict(preds, coef=[0.5, 1.5, 2.5, 3.5])
test_df.diagnosis = tst_pred.astype(int)
test_df.to_csv('submission.csv',index=False)
print('done' )<import_modules> | for name in train_ready:
print(name, "column entropy :", round(stats.entropy(train_ready[name].value_counts(normalize=True), base=2),2)) | Titanic - Machine Learning from Disaster |
7,705,538 | import tensorflow
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import cv2
import os<load_from_csv> | test = test.drop(columns = ['Cabin','Name','Ticket','PassengerId'])
test.head() | Titanic - Machine Learning from Disaster |
7,705,538 | train_df = pd.read_csv('.. /input/aptos2019-blindness-detection/train.csv')
train_df['id_code'] = train_df['id_code'].apply(lambda x:x+'.png')
train_df['diagnosis'] = train_df['diagnosis'].astype(str)
test_df = pd.read_csv('.. /input/aptos2019-blindness-detection/test.csv')
test_df['id_code'] = test_df['id_code'].a... | test['Age'].fillna(( test['Age'].mean()), inplace=True)
test['Fare'].fillna(( test['Fare'].mean()), inplace=True)
test.loc[ test['Fare'] <= 7.22, 'Fare'] = 0
test.loc[(test['Fare'] > 7.22)&(test['Fare'] <= 21.96), 'Fare'] = 1
test.loc[(test['Fare'] > 21.96)&(test['Fare'] <= 40.82), 'Fare'] = 2
test.loc[ test['Fare'] ... | Titanic - Machine Learning from Disaster |
7,705,538 | num_classes = train_df['diagnosis'].nunique()
TRAIN_DATA_ROOT = './train_images_preprocessed/'
TEST_DATA_ROOT = './test_images_preprocessed/'
BATCH_SIZE = 16
train_datagen = ImageDataGenerator(
rescale = 1/255,
rotation_range = 360,
horizontal_flip = True,
vertical_flip = True,
zoom_range = [0.98, 1.02],
width_shift_r... | test_dummies_Sex = pd.get_dummies(test.Sex)
test_dummies_Embarked = pd.get_dummies(test.Embarked)
test_ready = pd.concat([test, test_dummies_Sex,test_dummies_Embarked], axis=1)
test_ready.head()
test_ready = test_ready.drop(columns = ['Sex','Embarked'])
test_ready = test_ready.drop(columns = ['Age*Class'])
test_re... | Titanic - Machine Learning from Disaster |
7,705,538 | sklearn_class_weights = class_weight.compute_class_weight(
'balanced',
np.unique(train_generator.classes),
train_generator.classes)
print(sklearn_class_weights )<choose_model_class> | y = train_ready['Survived'].values
X = train_ready.drop('Survived',axis=1 ).values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state=21, stratify=y)
warnings.filterwarnings("ignore")
| Titanic - Machine Learning from Disaster |
7,705,538 | def create_resnet50_model(input_shape, n_out):
base_model = ResNet50(weights = None,
include_top = False,
input_shape = input_shape)
base_model.load_weights('.. /input/resnet50/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5')
model = Sequential()
model.add(base_model)
model.add(GlobalAveragePooling2D())
model... | clfs = []
seed = 3
clfs.append(( "LogReg", Pipeline([("Scaler", StandardScaler()),("LogReg", LogisticRegression())])))
clfs.append(( "XGBClassifier",Pipeline([("Scaler", StandardScaler()),("XGB", XGBClassifier())])))
clfs.append(( "KNN",Pipeline([("Scaler", StandardScaler()),("KNN", KNeighborsClassifier(n_neighbors=8... | Titanic - Machine Learning from Disaster |
7,705,538 | PRETRAINED_MODEL = '.. /input/pretrained_blindness_detector/blindness_detector.h5'
if(os.path.exists(PRETRAINED_MODEL)) :
print('Restoring model from ' + PRETRAINED_MODEL)
model.load_weights(PRETRAINED_MODEL)
else:
print('No pretrained model found.Using fresh model.')
current_epoch = 0<train_model> | scaler = StandardScaler()
scaler.fit(X)
scaled_features = scaler.transform(X)
train_sc = pd.DataFrame(scaled_features)
X_csv_test = test_ready.values
scaler.fit(X_csv_test)
scaled_features_test = scaler.transform(X_csv_test)
test_sc = pd.DataFrame(scaled_features_test)
scaled_features_test.shape | Titanic - Machine Learning from Disaster |
7,705,538 | <choose_model_class><EOS> | clf = xgb.XGBClassifier(n_estimators=250, random_state=4,bagging_fraction= 0.791787170136272, colsample_bytree= 0.7150126733821065,feature_fraction= 0.6929758008695552,gamma= 0.6716290491053838,learning_rate= 0.030240003246947006,max_depth= 2,min_child_samples= 5,num_leaves= 15,reg_alpha= 0.05822089056228967,reg_lambda... | Titanic - Machine Learning from Disaster |
7,718,019 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<define_variables> | train = pd.read_csv('.. /input/titanic/train.csv')
test = pd.read_csv('.. /input/titanic/test.csv')
gender_submission = pd.read_csv('.. /input/titanic/gender_submission.csv')
data = pd.concat([train, test], sort=False)
data['Sex'].replace(['male', 'female'], [0, 1], inplace=True)
data['Embarked'].fillna(( 'S'), in... | Titanic - Machine Learning from Disaster |
7,718,019 | !rm -rf /kaggle/working/train_images_preprocessed/
print("Preprocessing test images...")
!mkdir -p 'test_images_preprocessed'
for i, image_id in enumerate(tqdm(test_df['id_code'])) :
image = preprocess_image(f'.. /input/aptos2019-blindness-detection/test_images/{image_id}')
cv2.imwrite(f'./test_images_preprocessed/{i... | delete_columns = ['Name', 'PassengerId', 'Ticket', 'Cabin']
data.drop(delete_columns, axis=1, inplace=True)
train = data[:len(train)]
test = data[len(train):]
y_train = train['Survived']
X_train = train.drop('Survived', axis=1)
X_test = test.drop('Survived', axis=1 ) | Titanic - Machine Learning from Disaster |
7,718,019 | t_start = time.time()<set_options> | clf = LogisticRegression(penalty='l2', solver='sag', random_state=0 ) | Titanic - Machine Learning from Disaster |
7,718,019 | %reload_ext autoreload
%autoreload 2
%matplotlib inline<import_modules> | clf = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0 ) | Titanic - Machine Learning from Disaster |
7,718,019 | from fastai.vision import *
from fastai.metrics import error_rate
from fastai.callbacks import *<import_modules> | clf.fit(X_train, y_train)
y_pred = clf.predict(X_test ) | Titanic - Machine Learning from Disaster |
7,718,019 | from functools import partial
from sklearn import metrics
from collections import Counter<import_modules> | sub = pd.read_csv('.. /input/titanic/gender_submission.csv' ) | Titanic - Machine Learning from Disaster |
7,718,019 | import os
import PIL
import cv2<define_variables> | sub['Survived'] = list(map(int, y_pred))
sub.to_csv('submission_randomforest.csv', index=False)
sub.head() | Titanic - Machine Learning from Disaster |
7,718,019 | dat_path = Path('.. /input/aptos2019-blindness-detection')
trn_path = Path('.. /input/aptos2019-blindness-detection/train_images')
(dat_path,trn_path )<load_from_csv> | X_train, X_valid, y_train, y_valid = \
train_test_split(X_train, y_train, test_size=0.3,
random_state=0, stratify=y_train ) | Titanic - Machine Learning from Disaster |
7,718,019 | train_df = pd.read_csv(dat_path/'train.csv')
train_df.shape<train_on_grid> | categorical_features = ['Embarked', 'Pclass', 'Sex'] | Titanic - Machine Learning from Disaster |
7,718,019 | IMG_SIZE = 512
def _load_format(path, convert_mode, after_open)->Image:
image = cv2.imread(path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = crop_image_from_gray(image)
image = cv2.resize(image,(IMG_SIZE, IMG_SIZE))
image=cv2.addWeighted(image,4, cv2.GaussianBlur(image ,(0,0), 10),-4 ,128)
return Image(p... | lgb_train = lgb.Dataset(X_train, y_train,
categorical_feature=categorical_features)
lgb_eval = lgb.Dataset(X_valid, y_valid, reference=lgb_train,
categorical_feature=categorical_features)
params = {
'objective': 'binary'
}
model = lgb.train(params, lgb_train,
valid_sets=[lgb_train, lgb_eval],
verbose_eval=10,
num_boo... | Titanic - Machine Learning from Disaster |
7,718,019 | tfms = get_transforms(do_flip=True, flip_vert=True, max_rotate=0.10, max_zoom=1.3, max_warp=0.0, max_lighting=0.2 )<define_variables> | y_pred =(y_pred > 0.5 ).astype(int)
y_pred[:10] | Titanic - Machine Learning from Disaster |
7,718,019 | <create_dataframe><EOS> | sub['Survived'] = y_pred
sub.to_csv('submission_lightgbm.csv', index=False)
sub.head() | Titanic - Machine Learning from Disaster |
9,900,396 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<choose_model_class> | warnings.filterwarnings('ignore')
pd.options.display.max_columns = 40 | Titanic - Machine Learning from Disaster |
9,900,396 | kappa = KappaScore()
kappa.weights = "quadratic"<choose_model_class> | train_data = pd.read_csv("/kaggle/input/titanic/train.csv")
test_data = pd.read_csv("/kaggle/input/titanic/test.csv" ) | Titanic - Machine Learning from Disaster |
9,900,396 | learn = cnn_learner(data, models.resnet50, metrics=[error_rate, kappa] )<train_model> | full_dataset = pd.concat([train_data, test_data])
full_dataset.head() | Titanic - Machine Learning from Disaster |
9,900,396 | learn.fit_one_cycle(1 )<train_model> | class KnownCabinTransformer(BaseEstimator, TransformerMixin):
def __init__(self):
print("- KnownCabin transformer initiated -")
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
X.Cabin.fillna(0, inplace=True)
X["KnownCabin"] = 0
X.loc[X.Cabin != 0, 'KnownCabin'] = 1
print("- KnownCabin transfor... | Titanic - Machine Learning from Disaster |
9,900,396 | learn.fit_one_cycle(1)
<train_model> | class TitleTransformer(BaseEstimator, TransformerMixin):
def __init__(self):
print("- Title transformer initiated -")
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
X['Title'] = X.Name.str.extract('([A-Za-z]+)\.', expand=False)
X['Title'] = X['Title'].replace(['Lady', 'Countess','Capt', 'Col'... | Titanic - Machine Learning from Disaster |
9,900,396 | learn.unfreeze()
learn.fit_one_cycle(9, max_lr=slice(1e-4,1e-3))<define_variables> | class MissingFareTransformer(BaseEstimator, TransformerMixin):
def __init__(self):
print("- MissingFare transformer initiated -")
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
X.loc[(X.Pclass == 1)&(X.Fare.isnull()), 'Fare'] = X.loc[X.Pclass == 1]["Fare"].median()
X.loc[(X.Pclass == 2)&(X.Far... | Titanic - Machine Learning from Disaster |
9,900,396 | learn.model_dir = Path('/kaggle/working/' )<save_model> | class AgeStatusTransformer(BaseEstimator, TransformerMixin):
def __init__(self):
print("- AgeStatus transformer initiated -")
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
X["AgeStatus"] = "known"
X.loc[(X["Age"] > 1)&(( X["Age"]*2)%2 != 0), "AgeStatus"] = "estimated"
X.loc[X["Age"].isnull() ... | Titanic - Machine Learning from Disaster |
9,900,396 | learn.save('/kaggle/working/FastAI_APTOS_epoch_11')
learn.export('/kaggle/working/FastAI_APTOS_epoch_11.pkl' )<load_from_csv> | class TicketGroupingTransformer(BaseEstimator, TransformerMixin):
def __init__(self):
print("- TicketGrouping transformer initiated -")
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
cat_feature = ['Ticket']
count_enc = ce.CountEncoder(cols=cat_feature)
count_enc.fit(X[cat_feature])
grouping... | Titanic - Machine Learning from Disaster |
9,900,396 | test_df = pd.read_csv(dat_path/'sample_submission.csv' )<create_dataframe> | class AgeGuessingTransformer(BaseEstimator, TransformerMixin):
def __init__(self):
print("- AgeGuessing transformer initiated -")
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
X.loc[(X.Age.isnull() == True)&(X.Title_Master == 1), "Age"] = X.loc[X.Title_Master == 1]["Age"].median()
X.loc[(X.Ag... | Titanic - Machine Learning from Disaster |
9,900,396 | learn.data.add_test(ImageList.from_df(test_df,dat_path,folder='test_images',suffix='.png'))<init_hyperparams> | class AgeBinningTransformer(BaseEstimator, TransformerMixin):
def __init__(self):
print("- AgeBinning transformer initiated -")
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
self.bins = [0, 2, 16, 25, 40, 50, np.inf]
self.age_cat = ["0_2", "2_16", "16_25", "25_40", "30_50", "50+"]
X["AgeBins"... | Titanic - Machine Learning from Disaster |
9,900,396 | tta_params = {'beta':0.12, 'scale':1.0}<predict_on_test> | custom_pipeline = Pipeline([
("cabin_trans", KnownCabinTransformer()),
("title_trans", TitleTransformer()),
("hypo_missing_trans", HypotheticalMissingsTransformer()),
("missing_fare_trans", MissingFareTransformer()),
("embarked_trans", EmbarkedTransformer()),
("gender_trans", GenderTransformer()),
("class_trans"... | Titanic - Machine Learning from Disaster |
9,900,396 | preds,y = learn.get_preds(DatasetType.Test)
preds,y = learn.TTA(ds_type=DatasetType.Test, **tta_params )<prepare_output> | full_dataset["TS_Tragedy"] = "null"
i = 0
unique_tickets_subset = full_dataset.loc[full_dataset["Ticket_grouping"] > 2]["Ticket"].unique()
while i < len(unique_tickets_subset):
subset = full_dataset.loc[full_dataset.Ticket == unique_tickets_subset[i]]
try:
ratio = len(subset.loc[subset["Survived"] == 0])/ len(subset.lo... | Titanic - Machine Learning from Disaster |
9,900,396 | test_df.diagnosis = preds.argmax(1)
test_df.head()<save_to_csv> | full_dataset.set_index(full_dataset.PassengerId, verify_integrity = True, inplace=True)
full_dataset.tail() | Titanic - Machine Learning from Disaster |
9,900,396 | test_df.to_csv('submission.csv',index=False )<import_modules> | X_train = full_dataset.loc[full_dataset.Survived.isnull() == False]
y_train = X_train["Survived"].astype(int)
X_test = full_dataset.loc[full_dataset.Survived.isnull() ]
useless_features = ["PassengerId", "Survived", "Name", "Age", "Ticket", "Fare", "Cabin", "AgeBins"]
X_train.drop(useless_features, axis=1, inplace=Tru... | Titanic - Machine Learning from Disaster |
9,900,396 | from random import randint
import tensorflow as tf
import json
import os
from PIL import Image
from glob import glob
from zipfile import ZipFile
import pandas as pd
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint, Callback, EarlyStop... | scaler = StandardScaler()
X_train_scaled = pd.DataFrame(scaler.fit_transform(X_train[["SibSp","Parch","FamilySize","Ticket_grouping"]]), columns=["sc_SibSp","sc_Parch","sc_FamilySize","sc_Ticket_grouping"], index=X_train.index)
X_test_scaled = pd.DataFrame(scaler.fit_transform(X_test[["SibSp","Parch","FamilySize","Tic... | Titanic - Machine Learning from Disaster |
9,900,396 | def process_csv(dataframe: pd.DataFrame, image_column_name: str,
label_column_name: str,
folder_with_images: str)-> pd.DataFrame:
dataframe[image_column_name] = dataframe[image_column_name].apply(
lambda x: f"{folder_with_images}{x}.png")
dataframe[label_column_name] = dataframe[label_column_name].astype('str')
re... | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
import xgboost as xgb
import lightgbm as lgb
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import VotingC... | Titanic - Machine Learning from Disaster |
9,900,396 | train_datagen = ImageDataGenerator(rescale=1./ 255,
rotation_range=15,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.01,
zoom_range=[0.9, 1.25],
horizontal_flip=True,
vertical_flip=True,
fill_mode='reflect',
data_format='channels_last',
brightness_range=[0.5, 1.5],
validation_split=0.3 )<load_from_csv> | param_grid = [
{'n_neighbors':[2,3,4,5,6,7,8], 'weights':['uniform', 'distance'], 'n_jobs':[-1] }
]
neigh = KNeighborsClassifier()
grid_search = GridSearchCV(neigh, param_grid, cv=5, scoring='accuracy')
grid_search.fit(X_train, y_train)
grid_search.best_params_ | Titanic - Machine Learning from Disaster |
9,900,396 | train_csv = pd.read_csv("/kaggle/input/aptos2019-blindness-detection/train.csv")
train_csv = process_csv(
dataframe=train_csv,
image_column_name="id_code",
label_column_name="diagnosis",
folder_with_images="/kaggle/dataset_with_ben/" )<create_dataframe> | neigh = KNeighborsClassifier(n_neighbors = 5, weights = 'uniform')
neigh.fit(X_train, y_train)
scores = cross_val_score(neigh, X_train, y_train, scoring="accuracy", cv=10)
print(scores.mean() ) | Titanic - Machine Learning from Disaster |
9,900,396 | train_generator = train_datagen.flow_from_dataframe(
dataframe=train_csv, x_col="id_code", y_col="diagnosis", subset="training",
batch_size=32, target_size=(299, 299))
val_generator = train_datagen.flow_from_dataframe(
dataframe=train_csv, x_col="id_code", y_col="diagnosis",
subset="validation", batch_size=32, target... |
log_reg = LogisticRegression(C = 0.001, penalty='none')
log_reg.fit(X_train, y_train)
scores = cross_val_score(log_reg, X_train, y_train, scoring="accuracy", cv=10)
print("Logistic Regression mean score : ", scores.mean())
forest_clf = RandomForestClassifier(max_depth = 6, min_samples_leaf=1, min_samples_split=2,... | Titanic - Machine Learning from Disaster |
9,900,396 | class RAdam(Optimizer):
def __init__(self, lr, beta1=0.9, beta2=0.99, decay=0, **kwargs):
super(RAdam, self ).__init__(**kwargs)
with K.name_scope(self.__class__.__name__):
self.lr = K.variable(lr)
self._beta1 = K.variable(beta1, dtype="float32")
self._beta2 = K.variable(beta2, dtype="float32")
self._max_sma_length... | voting_clf = VotingClassifier(estimators=[('gbc', gbc),('xgb', xgb_clf),('forest', forest_clf),('svc', svc),('lrc', log_reg),('knc', neigh)],
voting='hard')
voting_clf.fit(X_train, y_train)
scores = cross_val_score(voting_clf, X_train, y_train, scoring="accuracy", cv=10)
print(scores.mean() ) | Titanic - Machine Learning from Disaster |
9,900,396 | sys.path.append(os.path.abspath('.. /input/kerasefficientnetsmaster/keras-efficientnets-master/keras-efficientnets-master/'))
def create_model() :
input_tensor = Input(( 299, 299, 3))
outputs = []
effnet = EfficientNetB7(input_shape=(299,299,3),
weights=sys.path.append(os.path.abspath('/kaggle/input/efficientnetb0b7-ke... | preds = voting_clf.predict(X_test)
output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': preds})
output.to_csv("submission.csv", index=False)
output.head() | Titanic - Machine Learning from Disaster |
9,900,396 | start_lr = 1e-10
end_lr = 1<compute_train_metric> | warnings.filterwarnings('ignore')
pd.options.display.max_columns = 40 | Titanic - Machine Learning from Disaster |
9,900,396 | def kappa_loss(y_pred, y_true, y_pow=2, eps=1e-10, N=5, bsize=256, name='kappa'):
with tf.name_scope(name):
y_true = tf.to_float(y_true)
repeat_op = tf.to_float(tf.tile(tf.reshape(tf.range(0, N), [N, 1]), [1, N]))
repeat_op_sq = tf.square(( repeat_op - tf.transpose(repeat_op)))
weights = repeat_op_sq / tf.to_float(... | train_data = pd.read_csv("/kaggle/input/titanic/train.csv")
test_data = pd.read_csv("/kaggle/input/titanic/test.csv" ) | Titanic - Machine Learning from Disaster |
9,900,396 | reduce_lr = ReduceLROnPlateau(monitor='val_acc', factor=0.2,
patience=5, min_lr=1e-5 )<choose_model_class> | full_dataset = pd.concat([train_data, test_data])
full_dataset.head() | Titanic - Machine Learning from Disaster |
9,900,396 | callbacks = [
ModelCheckpoint(
"best_weights.hdf5",
monitor='val_acc',
verbose=1, save_best_only=True,
save_weights_only=True),
EarlyStopping(monitor='val_acc', patience=5),
reduce_lr
]<train_model> | class KnownCabinTransformer(BaseEstimator, TransformerMixin):
def __init__(self):
print("- KnownCabin transformer initiated -")
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
X.Cabin.fillna(0, inplace=True)
X["KnownCabin"] = 0
X.loc[X.Cabin != 0, 'KnownCabin'] = 1
print("- KnownCabin transfor... | Titanic - Machine Learning from Disaster |
9,900,396 | model = create_model()
model.compile(optimizer=Adam(1e-4),
loss="categorical_crossentropy", metrics=["accuracy"])
model.fit_generator(generator=train_generator,
steps_per_epoch=len(train_generator),
validation_data=val_generator,
validation_steps=len(val_generator),
epochs=10,
callbacks=callbacks )<normalization> | class TitleTransformer(BaseEstimator, TransformerMixin):
def __init__(self):
print("- Title transformer initiated -")
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
X['Title'] = X.Name.str.extract('([A-Za-z]+)\.', expand=False)
X['Title'] = X['Title'].replace(['Lady', 'Countess','Capt', 'Col'... | Titanic - Machine Learning from Disaster |
9,900,396 | def test_time_augmentation(image, network_model):
datagen = ImageDataGenerator()
all_images = np.expand_dims(image, axis=0)
flip_horizontal_image = np.expand_dims(datagen.apply_transform(
x=image, transform_parameters={"flip_horizontal": True}), axis=0)
all_images = np.append(all_images, flip_horizontal_image, axis=... | class MissingFareTransformer(BaseEstimator, TransformerMixin):
def __init__(self):
print("- MissingFare transformer initiated -")
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
X.loc[(X.Pclass == 1)&(X.Fare.isnull()), 'Fare'] = X.loc[X.Pclass == 1]["Fare"].median()
X.loc[(X.Pclass == 2)&(X.Far... | Titanic - Machine Learning from Disaster |
9,900,396 | model.load_weights("best_weights.hdf5" )<save_to_csv> | class AgeStatusTransformer(BaseEstimator, TransformerMixin):
def __init__(self):
print("- AgeStatus transformer initiated -")
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
X["AgeStatus"] = "known"
X.loc[(X["Age"] > 1)&(( X["Age"]*2)%2 != 0), "AgeStatus"] = "estimated"
X.loc[X["Age"].isnull() ... | Titanic - Machine Learning from Disaster |
9,900,396 | test_csv = pd.read_csv("/kaggle/input/aptos2019-blindness-detection/test.csv")
predicted_csv = pd.DataFrame(columns=["id_code", "diagnosis"])
for id_code in test_csv["id_code"]:
filename = f"/kaggle/input/aptos2019-blindness-detection/test_images/{id_code}.png"
img = imread(filename)
img = cv2.resize(img, dsize=(299... | class TicketGroupingTransformer(BaseEstimator, TransformerMixin):
def __init__(self):
print("- TicketGrouping transformer initiated -")
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
cat_feature = ['Ticket']
count_enc = ce.CountEncoder(cols=cat_feature)
count_enc.fit(X[cat_feature])
grouping... | Titanic - Machine Learning from Disaster |
9,900,396 | sys.path.append(os.path.abspath('.. /input/efficientnet/efficientnet-master/efficientnet-master/'))
def create_effnetB5_model(input_shape, n_out):
model = Sequential()
base_model = EfficientNetB5(weights = None,
include_top = False,
input_shape = input_shape)
base_model.name = 'base_model'
model.add(base_model)
model... | class AgeGuessingTransformer(BaseEstimator, TransformerMixin):
def __init__(self):
print("- AgeGuessing transformer initiated -")
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
X.loc[(X.Age.isnull() == True)&(X.Title_Master == 1), "Age"] = X.loc[X.Title_Master == 1]["Age"].median()
X.loc[(X.Ag... | Titanic - Machine Learning from Disaster |
9,900,396 | PRETRAINED_MODEL = '.. /input/efficientnetb5-blindness-detector/blindness_detector_best_qwk.h5'
IMAGE_HEIGHT = 340
IMAGE_WIDTH = 340
num_classes = 5
class_text = ['Normal', 'Mild', 'Moderate', 'Severe', 'Proliferative']
print('Creating model...')
model = create_effnetB5_model(input_shape =(IMAGE_HEIGHT, IMAGE_WIDTH, 3... | class AgeBinningTransformer(BaseEstimator, TransformerMixin):
def __init__(self):
print("- AgeBinning transformer initiated -")
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
self.bins = [0, 2, 16, 25, 40, 50, np.inf]
self.age_cat = ["0_2", "2_16", "16_25", "25_40", "30_50", "50+"]
X["AgeBins"... | Titanic - Machine Learning from Disaster |
9,900,396 | submit = pd.read_csv('.. /input/aptos2019-blindness-detection/sample_submission.csv')
predicted = []
print("Making predictions...")
for i, name in tqdm(enumerate(submit['id_code'])) :
path = os.path.join('.. /input/aptos2019-blindness-detection/test_images/', name + '.png')
image = cv2.imread(path)
image = process_... | custom_pipeline = Pipeline([
("cabin_trans", KnownCabinTransformer()),
("title_trans", TitleTransformer()),
("hypo_missing_trans", HypotheticalMissingsTransformer()),
("missing_fare_trans", MissingFareTransformer()),
("embarked_trans", EmbarkedTransformer()),
("gender_trans", GenderTransformer()),
("class_trans"... | Titanic - Machine Learning from Disaster |
9,900,396 | submit['diagnosis'] = predicted
submit.to_csv('submission.csv', index = False)
submit.head(10 )<set_options> | full_dataset["TS_Tragedy"] = "null"
i = 0
unique_tickets_subset = full_dataset.loc[full_dataset["Ticket_grouping"] > 2]["Ticket"].unique()
while i < len(unique_tickets_subset):
subset = full_dataset.loc[full_dataset.Ticket == unique_tickets_subset[i]]
try:
ratio = len(subset.loc[subset["Survived"] == 0])/ len(subset.lo... | Titanic - Machine Learning from Disaster |
9,900,396 | %reload_ext autoreload
%autoreload 2
%matplotlib inline<import_modules> | full_dataset.set_index(full_dataset.PassengerId, verify_integrity = True, inplace=True)
full_dataset.tail() | Titanic - Machine Learning from Disaster |
9,900,396 | from fastai import *
from fastai.vision import *
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import os
import scipy as sp
from functools import partial
from sklearn import metrics
from collections import Counter
from fastai.callbacks import *
import PIL
import cv2<set_options> | X_train = full_dataset.loc[full_dataset.Survived.isnull() == False]
y_train = X_train["Survived"].astype(int)
X_test = full_dataset.loc[full_dataset.Survived.isnull() ]
useless_features = ["PassengerId", "Survived", "Name", "Age", "Ticket", "Fare", "Cabin", "AgeBins"]
X_train.drop(useless_features, axis=1, inplace=Tru... | Titanic - Machine Learning from Disaster |
9,900,396 | def seed_everything(seed=1358):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
seed_everything()<define_variables> | scaler = StandardScaler()
X_train_scaled = pd.DataFrame(scaler.fit_transform(X_train[["SibSp","Parch","FamilySize","Ticket_grouping"]]), columns=["sc_SibSp","sc_Parch","sc_FamilySize","sc_Ticket_grouping"], index=X_train.index)
X_test_scaled = pd.DataFrame(scaler.fit_transform(X_test[["SibSp","Parch","FamilySize","Tic... | Titanic - Machine Learning from Disaster |
9,900,396 | PATH = Path('.. /input/aptos2019-blindness-detection' )<load_from_csv> | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
import xgboost as xgb
import lightgbm as lgb
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import VotingC... | Titanic - Machine Learning from Disaster |
9,900,396 | df = pd.read_csv(PATH/'train.csv')
df.head()<count_values> | param_grid = [
{'n_neighbors':[2,3,4,5,6,7,8], 'weights':['uniform', 'distance'], 'n_jobs':[-1] }
]
neigh = KNeighborsClassifier()
grid_search = GridSearchCV(neigh, param_grid, cv=5, scoring='accuracy')
grid_search.fit(X_train, y_train)
grid_search.best_params_ | Titanic - Machine Learning from Disaster |
9,900,396 | df.diagnosis.value_counts()<feature_engineering> | neigh = KNeighborsClassifier(n_neighbors = 5, weights = 'uniform')
neigh.fit(X_train, y_train)
scores = cross_val_score(neigh, X_train, y_train, scoring="accuracy", cv=10)
print(scores.mean() ) | Titanic - Machine Learning from Disaster |
9,900,396 | tfms = get_transforms(do_flip=True, flip_vert=True, max_rotate=0.10, max_zoom=1.3, max_warp=0.0, max_lighting=0.2 )<normalization> |
log_reg = LogisticRegression(C = 0.001, penalty='none')
log_reg.fit(X_train, y_train)
scores = cross_val_score(log_reg, X_train, y_train, scoring="accuracy", cv=10)
print("Logistic Regression mean score : ", scores.mean())
forest_clf = RandomForestClassifier(max_depth = 6, min_samples_leaf=1, min_samples_split=2,... | Titanic - Machine Learning from Disaster |
9,900,396 | data =(
src.transform(tfms,size=128)
.databunch()
.normalize(imagenet_stats)
)
data<compute_train_metric> | voting_clf = VotingClassifier(estimators=[('gbc', gbc),('xgb', xgb_clf),('forest', forest_clf),('svc', svc),('lrc', log_reg),('knc', neigh)],
voting='hard')
voting_clf.fit(X_train, y_train)
scores = cross_val_score(voting_clf, X_train, y_train, scoring="accuracy", cv=10)
print(scores.mean() ) | Titanic - Machine Learning from Disaster |
9,900,396 | def quadratic_kappa(y_hat, y):
return torch.tensor(cohen_kappa_score(torch.round(y_hat), y, weights='quadratic'),device='cuda:0')
learn = cnn_learner(data, base_arch=models.resnet50 ,metrics=[quadratic_kappa],model_dir='/kaggle',pretrained=True )<find_best_params> | preds = voting_clf.predict(X_test)
output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': preds})
output.to_csv("submission.csv", index=False)
output.head() | Titanic - Machine Learning from Disaster |
2,067,866 | learn.lr_find()
learn.recorder.plot()<train_model> | %matplotlib inline
| Titanic - Machine Learning from Disaster |
2,067,866 | lr = 1e-2
learn.fit_one_cycle(3, lr )<normalization> | train=pd.read_csv(".. /input/train.csv")
test=pd.read_csv(".. /input/test.csv" ) | Titanic - Machine Learning from Disaster |
2,067,866 | learn.data = data =(
src.transform(tfms,size=224)
.databunch()
.normalize(imagenet_stats)
)
learn.lr_find()
learn.recorder.plot()<train_model> | data = [["Variable","Definition","Datatype","Key"],
["Passenger ID","Index for the observational unit","Int", "1-1309"],
["Pclass","boarding class of the observational unit","Int", "1-3"],
["Name","Name of observational unit, including the title","String","Multiple"],
["Sex","Gender of the observational unit","String",... | Titanic - Machine Learning from Disaster |
2,067,866 | lr = 1e-2
learn.fit_one_cycle(20, lr )<find_best_params> | train.isnull().any() | Titanic - Machine Learning from Disaster |
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