ML workflow (collect, prepare, train, evaluate, deploy) in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the first step in the ML workflow?

The first step is collecting data. This means gathering the information or examples the model will learn from.

beginner

Why do we prepare data before training a model?

Preparing data means cleaning and organizing it so the model can learn better. It helps remove mistakes and makes data easier to understand.

beginner

What happens during the training step in ML?

During training, the model looks at the data and learns patterns. It adjusts itself to make better predictions.

beginner

How do we know if a model is good after training?

We evaluate the model by testing it on new data and checking how well it predicts. This tells us if the model learned well.

beginner

What does deploying a model mean?

Deploying means putting the model into use so it can help in real life, like in an app or website.

What is the main goal of the data preparation step?

A To evaluate model accuracy

B To clean and organize data

C To deploy the model

D To collect new data

Data preparation is about cleaning and organizing data so the model can learn effectively.

Which step comes right after training a model?

A Evaluating the model

B Collecting data

C Deploying the model

D Preparing data

After training, we evaluate the model to see how well it learned.

What does deploying a model allow you to do?

A Collect more data

B Train the model faster

C Use the model in real applications

D Clean the data

Deploying puts the model into real use, like in apps or websites.

Why is collecting good data important?

A Because it trains the model automatically

B Because it makes deployment easier

C Because it speeds up evaluation

D Because bad data can confuse the model

Good data helps the model learn correctly; bad data can cause mistakes.

Which step involves checking model predictions on new data?

A Evaluating

B Collecting

C Training

D Preparing

Evaluation tests the model on new data to check its accuracy.

Describe the five main steps of the ML workflow and why each is important.

Think about what happens from start to finish when building an ML model.

Explain how data preparation affects the training and evaluation steps in ML.

Consider the role of clean data in learning and testing.

Practice

1. Which step in the ML workflow involves gathering raw data from various sources?

easy

A. Deploy

B. Train

C. Evaluate

D. Collect

Solution

1. Step 1: Understand the meaning of each ML workflow step

   Collect means to gather raw data needed for the model.

2. Step 2: Match the step to the description

   Since gathering data is the first step, it matches 'Collect'.

3. Final Answer:

   Collect -> Option D

4. Quick Check:

   Gathering data = Collect [OK]

Hint: Collect means get data first before anything else [OK]

Common Mistakes:

• Confusing 'Train' with data gathering
• Thinking 'Evaluate' means collecting data
• Mixing 'Deploy' with data collection

2. Which of the following is the correct order of the ML workflow steps?

easy

A. Collect, Prepare, Train, Evaluate, Deploy

B. Train, Collect, Prepare, Deploy, Evaluate

C. Prepare, Collect, Train, Evaluate, Deploy

D. Deploy, Evaluate, Train, Prepare, Collect

Solution

1. Step 1: Recall the standard ML workflow order

   The correct order is: Collect data, Prepare data, Train model, Evaluate model, Deploy model.

2. Step 2: Compare each option to the correct order

   Only Collect, Prepare, Train, Evaluate, Deploy matches this exact sequence.

3. Final Answer:

   Collect, Prepare, Train, Evaluate, Deploy -> Option A

4. Quick Check:

   Correct ML workflow order = Collect, Prepare, Train, Evaluate, Deploy [OK]

Hint: Remember: Collect first, then Prepare, Train, Evaluate, Deploy [OK]

Common Mistakes:

• Mixing up Collect and Prepare order
• Putting Deploy before Evaluate
• Starting with Train before data collection

3. Given this Python code snippet for training a model, what will be printed after evaluation?

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3, random_state=42)
model = DecisionTreeClassifier()
model.fit(X_train, y_train)
predictions = model.predict(X_test)
accuracy = accuracy_score(y_test, predictions)
print(f"Accuracy: {accuracy:.2f}")

medium

A. Accuracy: 0.98

B. Accuracy: 0.85

C. Accuracy: 0.70

D. Accuracy: 1.00

Solution

1. Step 1: Understand the code flow

   The code loads iris data, splits it, trains a Decision Tree, predicts, and calculates accuracy.

2. Step 2: Recall typical Decision Tree accuracy on iris test split

   With random_state=42 and 30% test size, accuracy is 0.98.

3. Final Answer:

   Accuracy: 0.98 -> Option A

4. Quick Check:

   Decision Tree on iris test = 0.98 accuracy [OK]

Hint: Decision Trees on iris usually score 98% accuracy [OK]

Common Mistakes:

• Assuming accuracy is always 1.00
• Confusing train and test accuracy
• Ignoring random_state effect on split

4. You wrote this code to train a model but get an error. What is the main problem?

from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)
print(model.predict(X_test))

Assuming X_train, y_train, and X_test are defined but you forgot to prepare data properly.

medium

A. Model was not imported correctly

B. Data was not scaled or cleaned before training

C. Predict method is called before fit

D. X_test has different features than X_train

Solution

1. Step 1: Analyze the error cause

   Logistic Regression often requires data preparation like scaling or cleaning.

2. Step 2: Identify missing preparation step

   Since data preparation is missing, model may fail or give poor results.

3. Final Answer:

   Data was not scaled or cleaned before training -> Option B

4. Quick Check:

   Missing data prep causes training issues [OK]

Hint: Always prepare data before training models [OK]

Common Mistakes:

• Thinking import error caused failure
• Assuming predict before fit error
• Ignoring feature mismatch possibility

5. You want to deploy a model that predicts house prices. Which step should you do right after evaluating the model's accuracy?

hard

A. Collect more data

B. Prepare the data again

C. Deploy the model to a real environment

D. Train the model with new parameters

Solution

1. Step 1: Understand the ML workflow sequence

   After evaluating the model, the next step is to deploy it if accuracy is good.

2. Step 2: Match the step after evaluation

   Deploy means putting the model into real use for predictions.

3. Final Answer:

   Deploy the model to a real environment -> Option C

4. Quick Check:

   Evaluation done, next is Deploy [OK]

Hint: Deploy comes right after evaluation if model is good [OK]

Common Mistakes:

• Repeating data collection unnecessarily
• Preparing data again without new data
• Retraining without evaluation feedback