---
title: Machine Learning Basic Concepts
description: "Machine Learning Terminology and Basic Concepts Before learning Machine Learning algorithms such as **Linear Regression, Logistic Regression, KNN, Decision Trees, and Neural Networks**, it is important to understand some basic Machine Learning terminology. The following diagram shows how the major…"
url: "https://csestudy247.com/docs/machine-learning-basic-concepts/"
updated: "2026-09-23"
category: ML
---

# Machine Learning Basic Concepts

Machine Learning Terminology and Basic Concepts

Before learning Machine Learning algorithms such as **Linear Regression, Logistic Regression, KNN, Decision Trees, and Neural Networks**, it is important to understand some basic Machine Learning terminology.

The following diagram shows how the major Machine Learning terms are connected.

 graph TD A\["📊 Dataset"\] A --> B\["📌 Samples"\] A --> C\["📥 Features (X)"\] A --> D\["🎯 Target / Label (y)"\] B --> E\["🏋️ Training Data"\] B --> F\["🧪 Testing Data"\] C --> E D --> E E --> G\["⚙️ ML Algorithm"\] G --> H\["🧠 Learned Model"\] H --> I\["📥 New Data"\] I --> J\["🔮 Prediction"\] G --> K\["Parameters"\] G --> L\["Hyperparameters"\] style A fill:#6f42c1,color:#fff,stroke:#333,stroke-width:3px style B fill:#005cbf,color:#fff,stroke:#333,stroke-width:3px style C fill:#005cbf,color:#fff,stroke:#333,stroke-width:3px style D fill:#e91e63,color:#fff,stroke:#333,stroke-width:3px style E fill:#ff9800,color:#fff,stroke:#333,stroke-width:3px style F fill:#ff9800,color:#fff,stroke:#333,stroke-width:3px style G fill:#6f42c1,color:#fff,stroke:#333,stroke-width:3px style H fill:#28a745,color:#fff,stroke:#333,stroke-width:4px style I fill:#005cbf,color:#fff,stroke:#333,stroke-width:3px style J fill:#28a745,color:#fff,stroke:#333,stroke-width:3px style K fill:#e91e63,color:#fff,stroke:#333,stroke-width:3px style L fill:#e91e63,color:#fff,stroke:#333,stroke-width:3px

---

## 1. Dataset

### Simple Explanation

  **Dataset** means a collection of data that is used for training, testing, or analyzing a Machine Learning model.

### Example

  📌 Student Placement Dataset

Suppose we have the following data:

| IQ | CGPA | Study Hours | Placement |
| --- | --- | --- | --- |
| 90 | 8.2 | 5 | Yes |
| 75 | 6.5 | 2 | No |
| 110 | 9.1 | 6 | Yes |

The complete table is called the **dataset**.

   **Exam Definition:** A dataset is a collection of data samples or observations used for training, testing, or evaluating a Machine Learning system.   **Remember:** Dataset = Collection of Data

---

## 2. Features

### Simple Explanation

  **Features** are the input variables given to a Machine Learning model to make a prediction.

### Example

In our student placement dataset:

- IQ
- CGPA
- Study Hours

These are the **features**.

 graph LR A\["📥 Features"\] A --> B\["IQ"\] A --> C\["CGPA"\] A --> D\["Study Hours"\] B --> E\["🧠 ML Model"\] C --> E D --> E E --> F\["🎯 Placement"\] style A fill:#005cbf,color:#fff,stroke:#333,stroke-width:3px style E fill:#6f42c1,color:#fff,stroke:#333,stroke-width:3px style F fill:#28a745,color:#fff,stroke:#333,stroke-width:3px  **Exam Definition:** A feature is an input variable or measurable characteristic used by a Machine Learning model to make predictions.   **Remember:** Features = Input

---

## 3. Target / Label

### Simple Explanation

  The **target** is the output that the Machine Learning model is trying to predict. In classification problems, the target is also commonly called the **label**.

### Example

In the student placement example:

- IQ → Feature
- CGPA → Feature
- Study Hours → Feature
- **Placement → Target / Label**

 graph LR A\["IQ"\] B\["CGPA"\] C\["Study Hours"\] A --> D\["🧠 ML Model"\] B --> D C --> D D --> E\["🎯 Placement Yes / No"\] style A fill:#005cbf,color:#fff,stroke:#333,stroke-width:3px style B fill:#005cbf,color:#fff,stroke:#333,stroke-width:3px style C fill:#005cbf,color:#fff,stroke:#333,stroke-width:3px style D fill:#6f42c1,color:#fff,stroke:#333,stroke-width:3px style E fill:#28a745,color:#fff,stroke:#333,stroke-width:3px  **Exam Definition:** The target is the output variable that a Machine Learning model learns to predict.   **Remember:** Features = Input
 Target = Output

---

## 4. Samples

### Simple Explanation

  A **sample** is one individual observation, example, or data point in a dataset.

### Example

| IQ | CGPA | Study Hours | Placement |
| --- | --- | --- | --- |
| **90** | **8.2** | **5** | **Yes** |
| 75 | 6.5 | 2 | No |

The first row represents **one sample**. If a dataset contains 10,000 rows, it generally contains **10,000 samples**.

  **Exam Definition:** A sample is a single observation, example, or data point in a dataset.   **Remember:** Sample = One Data Point / One Row

---

## 5. Training Data

### Simple Explanation

  **Training data** is the portion of the dataset used to teach the Machine Learning model. The model learns patterns and relationships from this data.  graph TD A\["📊 Complete Dataset"\] A --> B\["🏋️ Training Data"\] A --> C\["🧪 Testing Data"\] B --> D\["⚙️ ML Algorithm"\] D --> E\["🧠 Trained Model"\] style A fill:#6f42c1,color:#fff,stroke:#333,stroke-width:3px style B fill:#ff9800,color:#fff,stroke:#333,stroke-width:3px style C fill:#ff9800,color:#fff,stroke:#333,stroke-width:3px style D fill:#005cbf,color:#fff,stroke:#333,stroke-width:3px style E fill:#28a745,color:#fff,stroke:#333,stroke-width:4px

### Example

Suppose we have 1,000 samples.

- 800 samples → Training
- 200 samples → Testing

  **Exam Definition:** Training data is the portion of a dataset used to train a Machine Learning model and learn patterns from the data.

---

## 6. Testing Data

### Simple Explanation

  **Testing data** is a separate portion of the dataset used to evaluate the performance of a trained Machine Learning model on unseen data.  graph LR A\["🏋️ Training Data"\] B\["🧠 Trained Model"\] C\["🧪 Testing Data"\] D\["📊 Evaluation"\] A --> B B --> C C --> D style A fill:#ff9800,color:#fff,stroke:#333,stroke-width:3px style B fill:#6f42c1,color:#fff,stroke:#333,stroke-width:3px style C fill:#005cbf,color:#fff,stroke:#333,stroke-width:3px style D fill:#28a745,color:#fff,stroke:#333,stroke-width:3px  📌 Example

Dataset = 1,000 samples

Training = 800 samples

Testing = 200 samples

The model learns from the 800 training samples and is evaluated using the 200 testing samples.

   **Exam Definition:** Testing data is a separate portion of data used to evaluate a trained model on previously unseen examples.   **Remember:** Training Data → Learn
 Testing Data → Evaluate

---

## 7. Model

### Simple Explanation

  A **Machine Learning model** is a learned representation of patterns or relationships in the training data that can be used to make predictions on new data.  graph LR A\["📊 Training Data"\] B\["⚙️ Learning Algorithm"\] C\["🧠 Trained Model"\] A --> B B --> C style A fill:#ff9800,color:#fff,stroke:#333,stroke-width:3px style B fill:#005cbf,color:#fff,stroke:#333,stroke-width:3px style C fill:#28a745,color:#fff,stroke:#333,stroke-width:4px

### Example

Suppose we want to predict house prices. The model may learn the relationship between:

- House size
- Number of rooms
- Location
- House age

and the corresponding house price.

  **Exam Definition:** A model is a learned mathematical or computational representation of patterns in data used to make predictions.

---

## 8. Prediction

### Simple Explanation

  A **prediction** is the output produced by a trained Machine Learning model when new input data is provided.  graph LR A\["📥 New Data"\] B\["🧠 Trained Model"\] C\["🔮 Prediction"\] A --> B B --> C style A fill:#005cbf,color:#fff,stroke:#333,stroke-width:3px style B fill:#6f42c1,color:#fff,stroke:#333,stroke-width:3px style C fill:#28a745,color:#fff,stroke:#333,stroke-width:3px

### Example

  📌 Student Placement Prediction

New student:

- IQ = 95
- CGPA = 8.0
- Study Hours = 5

The trained model processes these features.

**Prediction → Placement = Yes**

   **Exam Definition:** Prediction is the output generated by a trained Machine Learning model for a given input.

---

## 9. Parameters

### Simple Explanation

  **Parameters** are values that a Machine Learning model learns automatically from the training data.

### Example: Linear Regression

Consider the equation:

**y = mx + c**

During training, the model learns values such as:

- **m** → coefficient / slope
- **c** → intercept

These learned values are **parameters**.

 graph LR A\["📊 Training Data"\] B\["⚙️ Training"\] C\["🔢 Learn Parameters"\] D\["🧠 Model"\] A --> B B --> C C --> D style A fill:#ff9800,color:#fff,stroke:#333,stroke-width:3px style B fill:#005cbf,color:#fff,stroke:#333,stroke-width:3px style C fill:#e91e63,color:#fff,stroke:#333,stroke-width:3px style D fill:#28a745,color:#fff,stroke:#333,stroke-width:4px  **Exam Definition:** A parameter is a value that is learned automatically by a Machine Learning model from training data.   **Remember:** Parameter = Model Learns It

---

## 10. Hyperparameters

### Simple Explanation

  **Hyperparameters** are configuration values that are set externally to control the learning process or model structure.

### Examples

- Learning Rate
- Number of Neighbors (K) in KNN
- Maximum Tree Depth
- Number of Trees
- Number of Epochs
- Batch Size
- Regularization Strength

  📌 Example: KNN

Suppose we choose:

**K = 5**

The value **5** is a hyperparameter. The model does not learn K automatically from the training examples in standard KNN; we choose it as part of configuring the algorithm.

   📌 Example: Gradient Descent

Suppose we choose:

**Learning Rate = 0.01**

The learning rate controls how large a step the optimization algorithm takes while updating model parameters.

   **Exam Definition:** A hyperparameter is a configuration value selected externally to control the learning process or structure of a Machine Learning model.   **Remember:** Hyperparameter = We Configure It

---

## 11. Parameters vs Hyperparameters

 graph TD A\["🧠 Machine Learning Model"\] A --> B\["Parameters"\] A --> C\["Hyperparameters"\] B --> B1\["Learned from Data"\] B --> B2\["Updated During Training"\] B --> B3\["Example: Weights"\] C --> C1\["Configured Externally"\] C --> C2\["Controls Training"\] C --> C3\["Example: Learning Rate"\] style A fill:#6f42c1,color:#fff,stroke:#333,stroke-width:3px style B fill:#005cbf,color:#fff,stroke:#333,stroke-width:3px style C fill:#e91e63,color:#fff,stroke:#333,stroke-width:3px

| Parameters | Hyperparameters |
| --- | --- |
| Learned from training data | Configured externally |
| Updated during training | Control how training/model behaves |
| Example: weights | Example: learning rate |
| Example: regression coefficients | Example: K in KNN |
| Example: neural network weights | Example: number of epochs |

  **Easy Trick:**

 Parameter → **Model learns it.**

 Hyperparameter → **We configure it.**

---

## 12. Complete Example: Student Placement Prediction

Now let's connect all the Machine Learning terminology using one complete example.

| IQ | CGPA | Study Hours | Placement |
| --- | --- | --- | --- |
| 90 | 8.2 | 5 | Yes |
| 75 | 6.5 | 2 | No |
| 110 | 9.1 | 6 | Yes |
| 85 | 7.2 | 3 | Yes |

 graph TD A\["📊 Student Dataset"\] A --> B\["📥 Features"\] A --> C\["🎯 Target"\] B --> B1\["IQ"\] B --> B2\["CGPA"\] B --> B3\["Study Hours"\] C --> C1\["Placement"\] B1 --> D\["🏋️ Training Data"\] B2 --> D B3 --> D C1 --> D D --> E\["⚙️ ML Algorithm"\] E --> F\["🧠 Trained Model"\] F --> G\["📥 New Student"\] G --> H\["🔮 Prediction"\] style A fill:#6f42c1,color:#fff,stroke:#333,stroke-width:3px style B fill:#005cbf,color:#fff,stroke:#333,stroke-width:3px style C fill:#e91e63,color:#fff,stroke:#333,stroke-width:3px style D fill:#ff9800,color:#fff,stroke:#333,stroke-width:3px style E fill:#005cbf,color:#fff,stroke:#333,stroke-width:3px style F fill:#28a745,color:#fff,stroke:#333,stroke-width:4px style G fill:#005cbf,color:#fff,stroke:#333,stroke-width:3px style H fill:#28a745,color:#fff,stroke:#333,stroke-width:3px

### Identify Each Term

| Term | In Our Example |
| --- | --- |
| Dataset | Complete student placement table |
| Sample | One student's row |
| Features | IQ, CGPA, Study Hours |
| Target / Label | Placement |
| Training Data | Data used to teach the model |
| Testing Data | Unseen data used for evaluation |
| Model | Learned relationship from training data |
| Prediction | Predicted Placement |
| Parameters | Values learned during training |
| Hyperparameters | Settings used to control training/model behavior |

---

## 13. Quick Revision for Exams

**Dataset:** Collection of data.

**Sample:** One observation or row.

**Feature:** Input variable.

**Target / Label:** Output to predict.

**Training Data:** Data used for learning.

**Testing Data:** Data used for evaluation.

**Model:** Learned representation of patterns.

**Prediction:** Output produced by the trained model.

**Parameter:** Value learned from training data.

**Hyperparameter:** Configuration value used to control the learning process.

## 14. One Diagram to Remember Everything

 graph TD A\["📊 DATASET"\] A --> B\["Samples"\] A --> C\["Features X"\] A --> D\["Target y"\] A --> E\["🏋️ Training Data"\] A --> F\["🧪 Testing Data"\] E --> G\["⚙️ ML Algorithm"\] G --> H\["🔢 Learn Parameters"\] G --> I\["⚙️ Hyperparameters"\] H --> J\["🧠 MODEL"\] I --> J J --> K\["📥 New Input"\] K --> L\["🔮 PREDICTION"\] F --> M\["📊 Evaluation"\] J --> M style A fill:#6f42c1,color:#fff,stroke:#333,stroke-width:3px style B fill:#005cbf,color:#fff,stroke:#333,stroke-width:3px style C fill:#005cbf,color:#fff,stroke:#333,stroke-width:3px style D fill:#e91e63,color:#fff,stroke:#333,stroke-width:3px style E fill:#ff9800,color:#fff,stroke:#333,stroke-width:3px style F fill:#ff9800,color:#fff,stroke:#333,stroke-width:3px style G fill:#005cbf,color:#fff,stroke:#333,stroke-width:3px style H fill:#e91e63,color:#fff,stroke:#333,stroke-width:3px style I fill:#e91e63,color:#fff,stroke:#333,stroke-width:3px style J fill:#28a745,color:#fff,stroke:#333,stroke-width:4px style K fill:#005cbf,color:#fff,stroke:#333,stroke-width:3px style L fill:#28a745,color:#fff,stroke:#333,stroke-width:4px style M fill:#ff9800,color:#fff,stroke:#333,stroke-width:3px

## Conclusion

Understanding these basic terms makes it much easier to learn Machine Learning algorithms. When you see a Machine Learning problem, first identify:

- What is the **dataset**?
- What are the **features (X)**?
- What is the **target (y)**?
- Which data is used for **training**?
- Which data is used for **testing**?
- What **model** is being trained?
- What **parameters** does it learn?
- Which **hyperparameters** control the learning?
- What **prediction** does the model produce?

  **Next Topic:** Train-Test Split and Validation in Machine Learning

## Machine Learning Basic Concepts MCQ

1. What is a dataset in Machine Learning?

- A collection of data used for learning, testing, or analysis
- Only the target variable
- Only the model parameters
- A Machine Learning algorithm

    A dataset contains the data samples used by a Machine Learning system.    [Discuss it](#)

Explanation

A dataset is a collection of data containing samples or observations used for training, testing, or analyzing a Machine Learning model.

2. What are features in Machine Learning?

- The final predictions produced by a model
- Input variables used by the model
- Only the training samples
- The model's accuracy

    Features provide the input information from which the model learns patterns.    [Discuss it](#)

Explanation

Features are input variables or measurable characteristics used by a Machine Learning model to make predictions.

3. What is the target variable in a Machine Learning problem?

- The input data
- The number of samples
- The output that the model learns to predict
- The learning rate

    The target represents the desired output of the prediction problem.    [Discuss it](#)

Explanation

The target is the output variable that a Machine Learning model learns to predict.

4. In a student placement dataset, IQ, CGPA, and study hours are features. What is the target if the goal is to predict placement?

- IQ
- CGPA
- Study Hours
- Placement

    The target is the value that the model is trying to predict.    [Discuss it](#)

Explanation

Placement is the target because the model uses IQ, CGPA, and study hours to predict whether the student will be placed.

5. What is a sample in a Machine Learning dataset?

- The complete dataset
- One individual observation or data point
- The target variable only
- The trained model

    Think of one row in a tabular dataset.    [Discuss it](#)

Explanation

A sample is one individual observation, example, or data point in a dataset. In a table, one row generally represents one sample.

6. What is the main purpose of training data?

- To evaluate the final model only
- To teach the model patterns from the data
- To store hyperparameters
- To generate random predictions

    Training data is the data from which the model learns.    [Discuss it](#)

Explanation

Training data is the portion of a dataset used to train the Machine Learning model and learn patterns or relationships.

7. What is the main purpose of testing data?

- To train the model again
- To change the target variable
- To evaluate the trained model on unseen data
- To automatically select all features

    Testing data should represent data the model did not learn from during training.    [Discuss it](#)

Explanation

Testing data is used to evaluate how well a trained Machine Learning model performs on previously unseen examples.

8. What is a Machine Learning model?

- A collection of raw data
- A learned representation of patterns or relationships in data
- A dataset containing only labels
- A hyperparameter

    The model is produced after the learning algorithm learns from training data.    [Discuss it](#)

Explanation

A Machine Learning model is a learned mathematical or computational representation of patterns in training data that can be used to make predictions.

9. What is a prediction in Machine Learning?

- The input feature before training
- The training dataset
- The output produced by a trained model for given input
- The model's hyperparameter

    A prediction is what the trained model produces when it receives input data.    [Discuss it](#)

Explanation

A prediction is the output generated by a trained Machine Learning model for a given input.

10. Which statement correctly describes a Machine Learning parameter?

- It is always manually selected before training
- It is a value learned by the model from training data
- It is always the target variable
- It is the number of samples in the dataset

    Parameters are learned and adjusted during model training.    [Discuss it](#)

Explanation

A parameter is a value learned automatically by a Machine Learning model from training data, such as weights or regression coefficients.

11. Which of the following is a hyperparameter?

- A neural network weight learned during training
- A regression coefficient learned from data
- Learning rate
- A predicted class

    Hyperparameters are configuration values used to control the learning process.    [Discuss it](#)

Explanation

Learning rate is a hyperparameter because it is configured externally and controls how the optimization algorithm updates model parameters.

12. Which statement correctly compares parameters and hyperparameters?

- Both are always learned automatically from training data
- Parameters are learned from data, while hyperparameters are configured externally
- Parameters are inputs and hyperparameters are targets
- Hyperparameters are always predictions

    Remember: parameters are learned by the model; hyperparameters control the learning process.    [Discuss it](#)

Explanation

Parameters are learned from training data, whereas hyperparameters are configured externally to control the learning process or model structure.

13. A dataset contains 10,000 rows. Generally, what does each row represent in a tabular Machine Learning dataset?

- One sample
- One algorithm
- One hyperparameter
- One model

    An individual observation in a dataset is called a sample.    [Discuss it](#)

Explanation

Each row generally represents one sample or observation, so a dataset with 10,000 rows generally contains 10,000 samples.

  [Discuss it](#)

Explanation

15. In the statement 'Features = Input and Target = Output', what does this relationship describe?

- How input variables are used to predict the desired output
- How testing data is deleted
- How hyperparameters become samples
- How predictions become training data automatically

    Features provide information to the model, while the target is the value being predicted.    [Discuss it](#)

Explanation

Features are the input variables supplied to the model, while the target is the output that the model learns to predict.