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Data Science & Machine Learning
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Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data
Mostrar más📈 Análisis del canal de Telegram Data Science & Machine Learning
El canal Data Science & Machine Learning (@datasciencefun) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 75 660 suscriptores, ocupando la posición 2 114 en la categoría Educación y el puesto 4 359 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 75 660 suscriptores.
Según los últimos datos del 11 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 911, y en las últimas 24 horas de 29, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 3.63%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.36% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 747 visualizaciones. En el primer día suele acumular 1 032 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 5.
- Intereses temáticos: El contenido se centra en temas clave como learning, accuracy, distribution, panda, dataset.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
For collaborations: @love_data”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 12 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.
75 660
Suscriptores
+2924 horas
+2107 días
+91130 días
Archivo de publicaciones
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✅ Cross Validation & Hyperparameter Tuning 🤖⚙️
👉 Building a model is not enough.
We must also make sure it performs well on unseen data.
This is done using:
✔ Cross Validation
✔ Hyperparameter Tuning
🔹 1. What is Cross Validation?
Cross Validation checks how well a model generalizes to new data.
👉 Instead of using only one train-test split, data is divided multiple times.
🔥 2. K-Fold Cross Validation ⭐
How it Works:
1️⃣ Split data into K parts (folds)
2️⃣ Use one fold for testing
3️⃣ Use remaining folds for training
4️⃣ Repeat until every fold is tested
✅ Example
If K = 5:
• 4 folds → Training
• 1 fold → Testing
Repeated 5 times.
🔹 3. Why Cross Validation is Important?
✔ Better model evaluation
✔ Reduces overfitting risk
✔ More reliable accuracy
🔹 4. Implementation (Python)
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
scores = cross_val_score(model, X, y, cv=5)
print(scores)
from sklearn.model_selection import GridSearchCV
params = {
"n_neighbors": [3,5,7]
}
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Which of the following can help reduce overfitting?
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✅ Overfitting vs Underfitting 🤖📉
👉 One of the most important concepts in Machine Learning.
A model should not:
❌ Learn too little
❌ Learn too much
It should learn just right ✅
🔹 1. What is Underfitting?
👉 Underfitting happens when the model is too simple and cannot learn patterns properly.
Characteristics:
❌ Poor performance on training data
❌ Poor performance on testing data
✅ Example
Trying to fit a straight line to highly complex data.
🔥 2. What is Overfitting?
👉 Overfitting happens when the model memorizes training data instead of learning general patterns.
Characteristics:
✔ Very high training accuracy
❌ Poor testing accuracy
✅ Example
A student memorizes answers instead of understanding concepts.
🔹 3. Ideal Model (Best Case) ⭐
👉 Performs well on:
✔ Training data
✔ Testing data
This is called: ✅ Good Generalization
🔹 4. Visual Understanding
📉 Underfitting → Too simple
📈 Overfitting → Too complex
✅ Balanced model → Best fit
🔹 5. Causes of Overfitting
✔ Too much model complexity
✔ Small dataset
✔ Too many features
🔹 6. How to Reduce Overfitting ⭐
✔ More training data
✔ Feature selection
✔ Cross-validation
✔ Regularization
✔ Simpler model
🔹 7. How to Reduce Underfitting
✔ Use better features
✔ Increase model complexity
✔ Train longer
🔹 8. Why This is Important?
✔ Critical interview topic
✔ Improves model performance
✔ Core ML concept
🎯 Today’s Goal
✔ Understand overfitting
✔ Understand underfitting
✔ Learn solutions
💬 Tap ❤️ for more!
In a good regression model, the R² score should be:
Which metric is commonly used for classification problems?
✅ Model Evaluation Metrics 📊🤖
👉 After building a Machine Learning model, we must check:
“How good is the model?”
This is done using evaluation metrics.
🔹 1. Why Model Evaluation is Important?
✔ Measures model performance
✔ Detects errors
✔ Helps compare models
✔ Prevents bad predictions
🔥 2. Evaluation Metrics for Regression
Used for predicting numbers
✅ MAE (Mean Absolute Error)
👉 Average absolute error.
MAE = (1/n) Σ |y - ŷ|
✔ Lower MAE = Better model
✅ MSE (Mean Squared Error)
👉 Squares the errors.
MSE = (1/n) Σ (y - ŷ)^2
✔ Punishes large errors more.
✅ RMSE (Root Mean Squared Error)
RMSE = √MSE = √[(1/n) Σ (y - ŷ)^2]
✔ Easy to interpret.
✅ R² Score ⭐
Measures how well model explains data.
R² = 1 - [Σ(y - ŷ)^2 / Σ(y - ȳ)^2]
R² = 1 → Perfect model
✔ Higher R² = Better performance
Where ŷ = predicted value, ȳ = mean of actual values
🔥 3. Evaluation Metrics for Classification
Used for categories
✅ Accuracy
Accuracy = Correct Predictions / Total Predictions
✅ Precision
👉 Out of predicted positives, how many are correct?
Precision = TP / (TP + FP)
✅ Recall
👉 Out of actual positives, how many detected?
Recall = TP / (TP + FN)
✅ F1-Score ⭐
Balance between precision & recall.
F1-Score = 2 (Precision × Recall) / (Precision + Recall)
🔹 4. Confusion Matrix ⭐
A table showing prediction results.
Actual Positive & Predicted Positive = TP (True Positive)
Actual Positive & Predicted Negative = FN (False Negative)
Actual Negative & Predicted Positive = FP (False Positive)
Actual Negative & Predicted Negative = TN (True Negative)
TP = model correctly predicted positive
TN = model correctly predicted negative
FP = model wrongly predicted positive
FN = model wrongly predicted negative
🔹 5. Implementation (Python)
from sklearn.metrics import accuracy_score
y_true = [0, 1, 1, 0]
y_pred = [0, 1, 0, 0]
print(accuracy_score(y_true, y_pred))
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Which library module is commonly used for PCA in Python?
