Comprehensive Machine Learning Resources Guide Random Forest, Decision Trees, SVM, F1 Score & Scikit-learn 📚 Official Scikit-learn Documentation Core Algorithm Documentation 📖 Academic Papers & Research
Book
Author(s)
Notes
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow Aurélien Géron
Covers DT, RF, SVM with practical code
Introduction to Statistical Learning (ISLR) James, Witten, Hastie, Tibshirani
FREE PDF available
The Elements of Statistical Learning Hastie, Tibshirani, Friedman
FREE PDF — Advanced mathematical foundations
Python Machine Learning Sebastian Raschka
Practical scikit-learn implementations
Machine Learning with Python Cookbook Chris Albon
Quick scikit-learn recipes
Interpretable Machine Learning Christoph Molnar
FREE — https://christophm.github.io/interpretable-ml-book/
Coursera : Build Decision Trees, SVMs, and Artificial Neural NetworksDataCamp : Random Forest, SVM, Decision Trees coursesPluralsight : ML courses with scikit-learnLinkedIn Learning : ML with Python coursesO'Reilly Learning : Comprehensive scikit-learn coursesIBM AI Engineering Professional Certificate
Channel
Focus
StatQuest with Josh Starmer Intuitive ML explanations with visuals
3Blue1Brown Mathematical visualizations
Sentdex ML with Python playlist
Tech With Tim ML tutorials with scikit-learn
Krish Naik SVM, Random Forest, Decision Trees
codebasics Scikit-learn tutorial series
PyData Conference — YouTube talks on scikit-learn best practicesSciPy Conference — ML tools presentations
💻 Tutorials & Practical Guides Support Vector Machines Resources F1 Score & Evaluation Metrics 🔬 Mathematical Foundations Gini Impurity (Decision Trees):
[
\text{Gini} = 1 - \sum_{i=1}^{n} p_i^2
]
Entropy (Decision Trees):
[
\text{Entropy} = -\sum_{i=1}^{n} p_i \log_2(p_i)
]
SVM Margin Maximization:
[
\text{Maximize: } \frac{2}{|w|}
]
F1 Score:
[
F_1 = 2 \times \frac{\text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}}
]
Precision & Recall:
[
\text{Precision} = \frac{TP}{TP + FP}, \quad \text{Recall} = \frac{TP}{TP + FN}
]
🔧 GitHub Repositories & Code Examples
Repository
Description
tanmayjay/Comparative-Analysis-of-Different-Classification-Algorithms Implements DT, RF, NB, SVM with confusion matrix and ROC
yemifalokun/comparingclassifiers UC Berkeley ML course comparing classifiers
willkoehrsen Random Forest implementation notebooks
calvinfeng GitBook Machine Learning Notebook with Gini index explanation
Model Interpretation Libraries Built-in Scikit-learn Datasets from sklearn .datasets import (
load_iris , # Classification
load_breast_cancer , # Binary classification
load_wine , # Multi-class classification
load_digits # Image classification
)🛠️ Key Scikit-learn Functions & Classes from sklearn .tree import DecisionTreeClassifier
from sklearn .ensemble import RandomForestClassifier
from sklearn .svm import SVC from sklearn .metrics import (
f1_score ,
precision_score ,
recall_score ,
accuracy_score ,
confusion_matrix ,
classification_report ,
roc_auc_score ,
RocCurveDisplay
)from sklearn .model_selection import (
cross_val_score ,
cross_val_predict ,
StratifiedKFold ,
GridSearchCV ,
HalvingGridSearchCV # Faster alternative
)from sklearn .tree import plot_tree , export_graphviz , export_text
from sklearn .inspection import permutation_importance , PartialDependenceDisplay 🎯 Hyperparameter Tuning Guide
Parameter
Description
Typical Range
n_estimatorsNumber of trees
100-1000
max_depthMaximum tree depth
5-50 or None
min_samples_splitMin samples to split
2-20
min_samples_leafMin samples at leaf
1-10
max_featuresFeatures per split
'sqrt', 'log2', int
Parameter
Description
Typical Range
CRegularization
0.1-100
gammaRBF kernel coefficient
'scale', 'auto', 0.001-1
kernelKernel type
'linear', 'rbf', 'poly', 'sigmoid'
⚖️ Algorithm Comparison Guide When to Use Each Algorithm
Criterion
Decision Tree
Random Forest
SVM
Dataset Size Small-Medium
Medium-Large
Small-Medium
High Dimensions Moderate
Good
Excellent
Interpretability Excellent
Moderate
Low
Training Speed Fast
Moderate
Slow for large data
Feature Importance Yes
Yes
No (native)
Overfitting Risk High
Low
Low
Memory Usage Low
High
Moderate
Performance Benchmarks (from research)
Random Forest: ~86-90% accuracy, F1 ~90%
Decision Tree: ~84-88% accuracy, F1 ~89%
SVM with RBF: ~85-92% (depends on scaling)
Always scale features before SVM (StandardScaler or MinMaxScaler)Use class_weight='balanced' for imbalanced datasets
Apply SMOTE for severe class imbalance
Use Random Forest as initial baseline model
Apply cross-validation with StratifiedKFold to preserve class distribution
Use f1_macro or f1_weighted for multi-class problems
Implement nested cross-validation for hyperparameter tuning
Overfitting Decision Trees (use max_depth, min_samples_leaf)
Not scaling data before SVM
Using accuracy on imbalanced datasets (use F1 instead)
Data leakage during preprocessing
🌐 Community & Forums
Stack Overflow — sklearn tag with 100k+ questionsCross Validated — https://stats.stackexchange.com/ (statistical ML)Scikit-learn GitHub Discussions — Official community support
r/MachineLearning
r/learnmachinelearning
🚀 Model Deployment Resources import joblib
# Save model
joblib .dump (model , 'model.pkl' )
# Load model
model = joblib .load ('model.pkl' )
Flask/FastAPI — REST API deploymentAWS SageMaker — Cloud ML deployment (supports scikit-learn)Docker — Containerized deploymentMLflow — Model tracking and deployment
