Hyper-parameters Optimization using Gridsearch and Crossvalidation

Cross-validation estimates how well a model generalizes to unseen data, without relying on a single lucky (or unlucky) train/validation split.

How k-Fold Cross-Validation Works

Hold out test data — Reserve a final test set never used during training or tuning.
Split training data into k folds — e.g. 5 equal partitions.
Rotate validation — For each fold:
- Use that fold as the validation set
- Train on the remaining k − 1 folds
- Record the validation score
Aggregate — Average the k scores for a stable performance estimate.
Tune — Repeat for each hyperparameter combination; pick the best average CV score.
Final evaluation — Retrain on all training data, evaluate once on the test set.

Why Use It?

Benefit	Explanation
Less variance	One random split can mislead; k folds smooth this out.
Better use of data	Every sample is used for both training and validation.
Safer tuning	CV guides hyperparameter search without peeking at the test set.

Common Variants

Stratified k-fold — Keeps class proportions in each fold (important for Titanic survival).
GridSearchCV / RandomizedSearchCV — Automate hyperparameter search with CV.
Leave-one-out (LOOCV) — k = n; expensive but uses maximum data per fold.

Key Rule

Never tune on the test set. Cross-validation uses training data only; the test set is evaluated once at the end.

			
# =============================================================================
# SECTION 1: DATA PREP, EDA & BASELINE DECISION TREE
# Run this cell to get ALL outputs for the Decision Tree section together.
# =============================================================================
# --- 1.1 Import libraries ---
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
%matplotlib inline
# --- 1.2 Load Titanic dataset ---
titanic = sns.load_dataset('titanic')
sns.get_dataset_names()
titanic.head()
# --- 1.3 Basic EDA ---
titanic.shape
titanic.info()
titanic.head()
titanic.isna().sum()
# --- 1.4 Drop unused columns ---
titanic.drop(columns=['who', 'adult_male', 'embark_town', 'alone',
                      'alive', 'class', 'deck'], inplace=True)
titanic.head()
titanic.isna().sum()
titanic.info()
# --- 1.5 Inspect missing embarked values ---
titanic[titanic['embarked'].isna()]
titanic['embarked'].mode()
# --- 1.6 Missing value treatment ---
titanic['embarked'] = titanic['embarked'].fillna(titanic['embarked'].mode()[0])
titanic.iloc[[61, 829], :]
age_missing = titanic[titanic['age'].isna()]
age_missing.sample(10)
age_missing.shape
titanic['age'].median()
titanic['age'] = titanic['age'].fillna(titanic['age'].median())
titanic.info()
titanic.iloc[[667, 368], :]
titanic.info()
# --- 1.7 Rename columns ---
titanic = titanic.rename(columns={"sex": "gender", "SibSp": "siblings", "Parch": "parents_child"})
titanic.head()
titanic.info()
# --- 1.8 Dummy coding of categorical variables ---
for col in titanic.columns:
    if titanic[col].dtype == "object":
        titanic[col] = pd.Categorical(titanic[col]).codes
titanic.head()
titanic.info()
# --- 1.9 Survival counts ---
titanic.groupby('survived').size()
# --- 1.10 Import sklearn ---
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn import tree
# --- 1.11 Create features and label ---
x = titanic.drop(['survived'], axis=1, inplace=False)
y = titanic['survived']
x.shape
print('\n')
y.shape
# --- 1.12 Train/test split ---
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=999)
x_train.shape
y_train.shape
x_test.shape
y_test.shape
x_train
y_train
# --- 1.13 Survival rate in train vs test ---
np.round(y_train.sum() / y_train.count() * 100, 2)
print('\n')
np.round(y_test.sum() / y_test.count() * 100, 2)
# --- 1.14 Create Decision Tree model ---
mytree = tree.DecisionTreeClassifier(
    criterion='gini', max_depth=3, min_samples_leaf=50, min_samples_split=75
)
mytree
# --- 1.15 Fit model ---
mytree.fit(x_train, y_train)
# --- 1.16 Predict ---
predicted = mytree.predict(x_test)
predicted
mytree.predict_proba(x_test)
# --- 1.17 Evaluate model performance ---
print(metrics.classification_report(y_test, predicted))
mytree
# --- 1.18 Confusion matrix and accuracy ---
df_confusion = metrics.confusion_matrix(y_test, predicted)
df_confusion
metrics.accuracy_score(y_test, predicted)
sns.heatmap(df_confusion, cmap='Blues',
            xticklabels=['Prediction No', 'Prediction Yes'],
            yticklabels=['Actual No', 'Actual Yes'],
            annot=True, fmt='d')
plt.show()
# --- 1.19 Feature importance and tree visualization ---
x.columns
mytree.feature_importances_
y
import graphviz
dot_data = tree.export_graphviz(
    mytree, out_file=None, feature_names=x.columns,
    class_names=['0', '1'], filled=True, rounded=True, special_characters=True
)
graph = graphviz.Source(dot_data)
graph

		

Output

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 15 columns):
 #   Column       Non-Null Count  Dtype   
---  ------       --------------  -----   
 0   survived     891 non-null    int64   
 1   pclass       891 non-null    int64   
 2   sex          891 non-null    object  
 3   age          714 non-null    float64 
 4   sibsp        891 non-null    int64   
 5   parch        891 non-null    int64   
 6   fare         891 non-null    float64 
 7   embarked     889 non-null    object  
 8   class        891 non-null    category
 9   who          891 non-null    object  
 10  adult_male   891 non-null    bool    
 11  deck         203 non-null    category
 12  embark_town  889 non-null    object  
 13  alive        891 non-null    object  
 14  alone        891 non-null    bool    
dtypes: bool(2), category(2), float64(2), int64(4), object(5)
memory usage: 80.7+ KB
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 8 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   survived  891 non-null    int64  
 1   pclass    891 non-null    int64  
 2   sex       891 non-null    object 
 3   age       714 non-null    float64
 4   sibsp     891 non-null    int64  
 5   parch     891 non-null    int64  
 6   fare      891 non-null    float64
 7   embarked  889 non-null    object 
dtypes: float64(2), int64(4), object(2)
memory usage: 55.8+ KB
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 8 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   survived  891 non-null    int64  
 1   pclass    891 non-null    int64  
 2   sex       891 non-null    object 
 3   age       891 non-null    float64
 4   sibsp     891 non-null    int64  
 5   parch     891 non-null    int64  
 6   fare      891 non-null    float64
 7   embarked  891 non-null    object 
dtypes: float64(2), int64(4), object(2)
memory usage: 55.8+ KB
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 8 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   survived  891 non-null    int64  
 1   pclass    891 non-null    int64  
 2   sex       891 non-null    object 
 3   age       891 non-null    float64
 4   sibsp     891 non-null    int64  
 5   parch     891 non-null    int64  
 6   fare      891 non-null    float64
 7   embarked  891 non-null    object 
dtypes: float64(2), int64(4), object(2)
memory usage: 55.8+ KB
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 8 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   survived  891 non-null    int64  
 1   pclass    891 non-null    int64  
 2   gender    891 non-null    object 
 3   age       891 non-null    float64
 4   sibsp     891 non-null    int64  
 5   parch     891 non-null    int64  
 6   fare      891 non-null    float64
 7   embarked  891 non-null    object 
dtypes: float64(2), int64(4), object(2)
memory usage: 55.8+ KB
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 8 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   survived  891 non-null    int64  
 1   pclass    891 non-null    int64  
 2   gender    891 non-null    int8   
 3   age       891 non-null    float64
 4   sibsp     891 non-null    int64  
 5   parch     891 non-null    int64  
 6   fare      891 non-null    float64
 7   embarked  891 non-null    int8   
dtypes: float64(2), int64(4), int8(2)
memory usage: 43.6 KB




              precision    recall  f1-score   support

           0       0.77      0.99      0.86       115
           1       0.97      0.45      0.62        64

    accuracy                           0.80       179
   macro avg       0.87      0.72      0.74       179
weighted avg       0.84      0.80      0.78       179

			
# =============================================================================
# SECTION 2: CROSS-VALIDATION DEMO
# Requires Section 1 to be run first (mytree, x_train, y_train).
# =============================================================================
from sklearn.model_selection import cross_val_score
# --- 2.1 Default scoring (accuracy) ---
scores_acc = cross_val_score(mytree, x_train, y_train, cv=5)
scores_acc
# --- 2.2 Recall scoring ---
scores = cross_val_score(mytree, x_train, y_train, cv=5, scoring='recall')
scores

		

Output

array([0.71328671, 0.74125874, 0.78873239, 0.75352113, 0.74647887])

array([0.73214286, 0.67857143, 0.66071429, 0.63636364, 0.69090909])

			
# =============================================================================
# SECTION 3: HYPERPARAMETER OPTIMIZATION (GridSearchCV — Decision Tree)
# Requires Section 1 to be run first.
# =============================================================================
from sklearn.model_selection import GridSearchCV
# --- 3.1 Reset tree for tuning ---
mytree = tree.DecisionTreeClassifier(random_state=99, class_weight='balanced')
mytree
# --- 3.2 Define hyperparameter search space ---
my_max_depth = [2, 3, 4, 5, 10]
my_criterion = ['gini', 'entropy']
my_min_samples_leaf = [2, 5, 10, 15, 20, 25]
my_min_samples_split = [2, 5, 10, 15, 50, 100]
len(my_max_depth) * len(my_criterion) * len(my_min_samples_leaf) * len(my_min_samples_split)
# --- 3.3 Build GridSearchCV ---
grid = GridSearchCV(
    estimator=mytree, cv=5, scoring='recall',
    param_grid=dict(
        max_depth=my_max_depth, criterion=my_criterion,
        min_samples_leaf=my_min_samples_leaf,
        min_samples_split=my_min_samples_split
    )
)
grid
# --- 3.4 Fit on training data ---
grid.fit(x_train, y_train)
grid
# --- 3.5 Best model results ---
grid.best_params_
grid.best_estimator_.feature_importances_
print()
x.columns
grid.predict(x_test)
np.round(grid.best_score_, 2) * 100
results = pd.DataFrame(grid.cv_results_)
results.to_csv("results.csv")
# --- 3.6 Evaluate tuned model on test set ---
predicted = grid.predict(x_test)
print(metrics.classification_report(y_test, predicted))

		

Output

DecisionTreeClassifier

DecisionTreeClassifier(class_weight='balanced', random_state=99)

GridSearchCV

estimator: DecisionTreeClassifier

DecisionTreeClassifier

GridSearchCV

best_estimator_: DecisionTreeClassifier

DecisionTreeClassifier

GridSearchCV

best_estimator_: DecisionTreeClassifier

DecisionTreeClassifier

{'criterion': 'gini',
 'max_depth': 5,
 'min_samples_leaf': 20,
 'min_samples_split': 50}

array([0.17766848, 0.64657247, 0.08641906, 0.        , 0.        ,
       0.06719105, 0.02214894])

Index(['pclass', 'gender', 'age', 'sibsp', 'parch', 'fare', 'embarked'], dtype='object')

array([0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0,
       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
       0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
       1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0,
       1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1,
       0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1,
       0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0,
       1, 0, 0])

np.float64(81.0)

              precision    recall  f1-score   support

           0       0.80      0.88      0.84       115
           1       0.74      0.61      0.67        64

    accuracy                           0.78       179
   macro avg       0.77      0.74      0.75       179
weighted avg       0.78      0.78      0.78       179

			
# =============================================================================
# SECTION 4: PREDICT PROBABILITY OF CLASSES
# Requires Section 3 to be run first (grid, predicted).
# =============================================================================
predicted_prob = grid.predict_proba(x_test)
predicted_prob_df = pd.DataFrame(predicted_prob)
predicted_classes_df = pd.DataFrame(predicted)
y_actual_df = pd.DataFrame(y_test.values)
predicted_df = pd.concat([predicted_prob_df, predicted_classes_df, y_actual_df], axis=1)
predicted_df.columns = ['Prob_0', 'Prob_1', 'Predicted_Class', 'Actual_Class']
predicted_prob
predicted_prob[0, 1]
predicted_df.sample(20)

		

Output

array([[0.74243323, 0.25756677],
[0.88857796, 0.11142204],
[0.88857796, 0.11142204],
[0.88857796, 0.11142204],
[0.4082232 , 0.5917768 ],
[0.74243323, 0.25756677],
[0.31391147, 0.68608853],
[0.74243323, 0.25756677],
[0.45582562, 0.54417438],
[0.85217984, 0.14782016],
[0.88857796, 0.11142204],
[0. , 1. ],
[0.88857796, 0.11142204],
[0.71187405, 0.28812595],
[0. , 1. ],
[0.30722004, 0.69277996],
[0.15470228, 0.84529772],
[0.71187405, 0.28812595],
[0.85217984, 0.14782016],
[0.88857796, 0.11142204],
[0.88857796, 0.11142204],
[0.56161616, 0.43838384],
[0.88857796, 0.11142204],
[0.88857796, 0.11142204],
[0.45582562, 0.54417438],
[0.88857796, 0.11142204],
[0.71927555, 0.28072445],
[0.88857796, 0.11142204],
[0.88857796, 0.11142204],
[0.88857796, 0.11142204],
[0.85217984, 0.14782016],
[0.88857796, 0.11142204],
[0.56161616, 0.43838384],
[0.56161616, 0.43838384],
[0.88857796, 0.11142204],
[0.88857796, 0.11142204],
[0.74243323, 0.25756677],
[0.88857796, 0.11142204],
[0.88857796, 0.11142204],
[0.88857796, 0.11142204],
[0.88857796, 0.11142204],
[0.10155869, 0.89844131],
[0.88857796, 0.11142204],
[0.56161616, 0.43838384],
[0.74243323, 0.25756677],
[0.88857796, 0.11142204],
[0. , 1. ],
[0.88857796, 0.11142204],
[0.88857796, 0.11142204],
[0.88857796, 0.11142204],
[0. , 1. ],
[0.71187405, 0.28812595],
[0.88857796, 0.11142204],
[0.28589058, 0.71410942],

	Prob_0	Prob_1	Predicted_Class	Actual_Class
20	0.888578	0.111422	0	0
88	0.455826	0.544174	1	0
98	0.888578	0.111422	0	0
1	0.888578	0.111422	0	0
59	0.711874	0.288126	0	1
147	0.888578	0.111422	0	1
5	0.742433	0.257567	0	0
25	0.888578	0.111422	0	0
142	0.408223	0.591777	1	0
7	0.742433	0.257567	0	0
72	0.046959	0.953041	1	1
164	0.888578	0.111422	0	0
89	0.711874	0.288126	0	0
9	0.852180	0.147820	0	0
43	0.561616	0.438384	0	1
120	0.711874	0.288126	0	1
22	0.888578	0.111422	0	0
21	0.561616	0.438384	0	1
146	0.455826	0.544174	1	0
144	0.888578	0.111422	0	0

			
# =============================================================================
# SECTION 5: THRESHOLD OPTIMIZATION
# Requires Section 3 to be run first (grid).
# =============================================================================
predicted_prob = grid.predict_proba(x_test)
predicted_prob
# --- 5.1 Threshold = 0.25 ---
new_y_test = predicted_prob[:, 1] >= 0.25
new_y_test
print(metrics.classification_report(y_test, new_y_test))
# --- 5.2 Threshold = 0.9 ---
new_y_test = predicted_prob[:, 1] >= 0.9
new_y_test
print(metrics.classification_report(y_test, new_y_test))

		

Output

array([[0.74243323, 0.25756677],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.4082232 , 0.5917768 ],
       [0.74243323, 0.25756677],
       [0.31391147, 0.68608853],
       [0.74243323, 0.25756677],
       [0.45582562, 0.54417438],
       [0.85217984, 0.14782016],
       [0.88857796, 0.11142204],
       [0.        , 1.        ],
       [0.88857796, 0.11142204],
       [0.71187405, 0.28812595],
       [0.        , 1.        ],
       [0.30722004, 0.69277996],
       [0.15470228, 0.84529772],
       [0.71187405, 0.28812595],
       [0.85217984, 0.14782016],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.56161616, 0.43838384],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.45582562, 0.54417438],
       [0.88857796, 0.11142204],
       [0.71927555, 0.28072445],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.85217984, 0.14782016],
       [0.88857796, 0.11142204],
       [0.56161616, 0.43838384],
       [0.56161616, 0.43838384],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.74243323, 0.25756677],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.10155869, 0.89844131],
       [0.88857796, 0.11142204],
       [0.56161616, 0.43838384],
       [0.74243323, 0.25756677],
       [0.88857796, 0.11142204],
       [0.        , 1.        ],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.        , 1.        ],
       [0.71187405, 0.28812595],
       [0.88857796, 0.11142204],
       [0.28589058, 0.71410942],
       [0.4082232 , 0.5917768 ],
       [0.30722004, 0.69277996],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.71187405, 0.28812595],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.04695946, 0.95304054],
       [0.56161616, 0.43838384],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.04695946, 0.95304054],
       [0.71187405, 0.28812595],
       [0.88857796, 0.11142204],
       [0.56161616, 0.43838384],
       [0.04695946, 0.95304054],
       [0.4082232 , 0.5917768 ],
       [0.88857796, 0.11142204],
       [0.71187405, 0.28812595],
       [0.56161616, 0.43838384],
       [0.85217984, 0.14782016],
       [0.88857796, 0.11142204],
       [0.71187405, 0.28812595],
       [0.28589058, 0.71410942],
       [0.71187405, 0.28812595],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.45582562, 0.54417438],
       [0.71187405, 0.28812595],
       [0.88857796, 0.11142204],
       [0.74243323, 0.25756677],
       [0.85217984, 0.14782016],
       [0.88857796, 0.11142204],
       [0.45582562, 0.54417438],
       [0.        , 1.        ],
       [0.85217984, 0.14782016],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.28589058, 0.71410942],
       [0.71187405, 0.28812595],
       [0.88857796, 0.11142204],
       [0.45582562, 0.54417438],
       [0.88857796, 0.11142204],
       [0.4082232 , 0.5917768 ],
       [0.        , 1.        ],
       [0.15470228, 0.84529772],
       [0.88857796, 0.11142204],
       [0.        , 1.        ],
       [0.88857796, 0.11142204],
       [0.04695946, 0.95304054],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.06644359, 0.93355641],
       [0.88857796, 0.11142204],
       [0.28589058, 0.71410942],
       [0.88857796, 0.11142204],
       [0.        , 1.        ],
       [0.88857796, 0.11142204],
       [0.        , 1.        ],
       [0.71187405, 0.28812595],
       [0.        , 1.        ],
       [0.71187405, 0.28812595],
       [0.88857796, 0.11142204],
       [0.71187405, 0.28812595],
       [0.30722004, 0.69277996],
       [0.88857796, 0.11142204],
       [0.        , 1.        ],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.74243323, 0.25756677],
       [0.04695946, 0.95304054],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.        , 1.        ],
       [0.88857796, 0.11142204],
       [0.45582562, 0.54417438],
       [0.56161616, 0.43838384],
       [0.88857796, 0.11142204],
       [0.        , 1.        ],
       [0.71187405, 0.28812595],
       [0.4082232 , 0.5917768 ],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.        , 1.        ],
       [0.45582562, 0.54417438],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.71187405, 0.28812595],
       [0.06644359, 0.93355641],
       [0.10155869, 0.89844131],
       [0.88857796, 0.11142204],
       [0.06644359, 0.93355641],
       [0.71187405, 0.28812595],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.71187405, 0.28812595],
       [0.28589058, 0.71410942],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.30722004, 0.69277996],
       [0.88857796, 0.11142204],
       [0.71927555, 0.28072445],
       [0.88857796, 0.11142204],
       [0.56161616, 0.43838384],
       [0.        , 1.        ],
       [0.4082232 , 0.5917768 ],
       [0.88857796, 0.11142204],
       [0.88857796, 0.11142204],
       [0.71927555, 0.28072445],
       [0.04695946, 0.95304054],
       [0.88857796, 0.11142204],
       [0.56161616, 0.43838384],
       [0.28589058, 0.71410942],
       [0.88857796, 0.11142204],
       [0.10155869, 0.89844131],
       [0.88857796, 0.11142204],
       [0.85217984, 0.14782016]])

array([ True, False, False, False,  True,  True,  True,  True,  True,
       False, False,  True, False,  True,  True,  True,  True,  True,
       False, False, False,  True, False, False,  True, False,  True,
       False, False, False, False, False,  True,  True, False, False,
        True, False, False, False, False,  True, False,  True,  True,
       False,  True, False, False, False,  True,  True, False,  True,
        True,  True, False, False, False,  True, False, False, False,
       False, False, False, False,  True,  True, False, False, False,
        True,  True, False,  True,  True,  True, False,  True,  True,
       False, False,  True,  True,  True, False, False,  True,  True,
       False,  True, False, False,  True,  True, False, False, False,
        True,  True, False,  True, False,  True,  True,  True, False,
        True, False,  True, False, False,  True, False,  True, False,
        True, False,  True,  True,  True,  True, False,  True,  True,
       False,  True, False, False,  True,  True, False, False, False,
        True, False,  True,  True, False,  True,  True,  True, False,
       False,  True,  True, False, False,  True,  True,  True, False,
        True,  True, False, False,  True,  True, False, False,  True,
       False,  True, False,  True,  True,  True, False, False,  True,
        True, False,  True,  True, False,  True, False, False])

              precision    recall  f1-score   support

           0       0.85      0.66      0.75       115
           1       0.57      0.80      0.66        64

    accuracy                           0.71       179
   macro avg       0.71      0.73      0.70       179
weighted avg       0.75      0.71      0.72       179

array([False, False, False, False, False, False, False, False, False,
       False, False,  True, False, False,  True, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False,  True, False, False, False,  True, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False,  True, False, False, False, False,
        True, False, False, False,  True, False, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False,  True, False, False, False,
       False, False, False, False, False, False,  True, False, False,
        True, False,  True, False, False,  True, False, False, False,
        True, False,  True, False,  True, False, False, False, False,
       False,  True, False, False, False,  True, False, False, False,
        True, False, False, False, False,  True, False, False, False,
       False,  True, False, False, False, False,  True, False, False,
        True, False, False, False, False, False, False, False, False,
       False, False, False, False,  True, False, False, False, False,
        True, False, False, False, False, False, False, False])

              precision    recall  f1-score   support

           0       0.74      1.00      0.85       115
           1       1.00      0.38      0.55        64

    accuracy                           0.78       179
   macro avg       0.87      0.69      0.70       179
weighted avg       0.83      0.78      0.74       179

RP’s Blog on AI

Connect with RP- https://www.linkedin.com/in/ratnakarpandey/

Hyper-parameters Optimization using Gridsearch and Crossvalidation

How k-Fold Cross-Validation Works

Why Use It?

Common Variants

Key Rule

How k-Fold Cross-Validation Works

Why Use It?

Common Variants

Key Rule

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