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Artificial intelligence (AI) with It's Applications

The book "Artificial Intelligence (AI) with It's Applications" provides a comprehensive insight into the field of AI, exploring its fundamental principles, modern applications, and future potential. It serves as a valuable resource for students, researchers, and professionals looking to understand AI’s role in shaping industries and everyday life. The book begins with an introduction to Artificial Intelligence , cov…

Licence
OPEN CC-BY-4.0
Authors
Dr. Dipikaben Umakant Thakar, Mrs. PL. Natchiammai, Dr. R. J. Kavitha…
Published
2025-03-18 · Zenodo
Language
eng
Length
66700 words
Type
narrative text
Open ↗ Download Open original ↗

Normalizing the features for stable and fast training.

scaler = StandardScaler()

X_train = scaler.fit_transform(X_train)

X_val = scaler.transform(X_val)

Now let’s train some state-of-the-art machine learning models and compare them which fit better with our data.

from sklearn.metrics import mean_absolute_error as mae

models = [LinearRegression(), XGBRegressor(),

Lasso(), RandomForestRegressor(), Ridge()]

for i in range(5):

models[i].fit(X_train, Y_train)

print(f'{models[i]} : ')

train_preds = models[i].predict(X_train)

print('Training Error : ', mae(Y_train, train_preds))

val_preds = models[i].predict(X_val)

print('Validation Error : ', mae(Y_val, val_preds))

print()

Output

LinearRegression() :

Training Error : 17.893463692619434

Validation Error : 18.007896272831253

XGBRegressor(base_score=None, booster=None, callbacks=None,

colsample_bylevel=None, colsample_bynode=None,

colsample_bytree=None, device=None, early_stopping_rounds=None,

enable_categorical=False, eval_metric=None, feature_types=None,

gamma=None, grow_policy=None, importance_type=None,

interaction_constraints=None, learning_rate=None, max_bin=None,

max_cat_threshold=None, max_cat_to_onehot=None,

max_delta_step=None, max_depth=None, max_leaves=None,

min_child_weight=None, missing=nan, monotone_constraints=None,

multi_strategy=None, n_estimators=None, n_jobs=None,

num_parallel_tree=None, random_state=None, ...) :

Training Error : 7.89463304294701

Validation Error : 10.12050432946533

Lasso() :

Training Error : 17.915089584958036

Validation Error : 17.995033362288662

RandomForestRegressor() :

Training Error : 3.9877936746031746

Validation Error : 10.451300301587302

Ridge() :

Training Error : 17.893530494767777

Validation Error : 18.00781790803129