1.What is Machine Learning?
Machine learning is a branch of artificial intelligence that
focuses on developing algorithms and statistical models, enabling computer
systems to learn and make predictions or decisions without being explicitly
programmed. It involves the study of algorithms and statistical models that
computers use to perform tasks and improve their performance through
experience.
2.What are the types of Machine Learning?
There are three main types of machine learning:
a) Supervised Learning: In this type, the algorithm learns
from labeled data, making predictions or classifications based on past
examples.
b) Unsupervised Learning: Here, the algorithm learns fromunlabeled data, discovering patterns and relationships without any predefined
outputs.
c) Reinforcement Learning: This type involves an agent
learning through trial and error interactions with an environment to maximize
rewards.
3.What is the difference between Overfitting and
Underfitting?
Overfitting occurs when a model learns too much from the
training data, leading to poor performance on new, unseen data. It happens when
the model becomes too complex, capturing noise or irrelevant patterns in the
training set. Underfitting, on the other hand, occurs when a model fails to
capture the underlying patterns in the data, resulting in high bias and low
accuracy.
4.Explain the Bias-Variance Tradeoff.
The bias-variance tradeoff is a fundamental concept in
machine learning. Bias refers to the error introduced by approximating a
real-world problem with a simplified model, while variance measures the model’s
sensitivity to fluctuations in the training data. A high-bias model tends to
underfit, while a high-variance model tends to overfit. Achieving an optimal
balance between bias and variance is crucial for building a well-performing
machine learning model.
5.What are the evaluation metrics used for assessing a
machine learning model?
Common evaluation metrics in machine learning include
accuracy, precision, recall, F1 score, and area under the receiver operating
characteristic curve (AUC-ROC). The choice of metric depends on the problem at
hand, such as classification, regression, or clustering.
6.What is the difference between Bagging and Boosting?
Bagging and boosting are ensemble learning techniques that
combine multiple machine learning models for improved performance. In bagging,
multiple models are trained independently on different subsets of the training
data, and their predictions are combined through averaging or voting. Boosting,
on the other hand, trains models sequentially, where each subsequent model
focuses on the mistakes made by the previous models, resulting in a strongerfinal model.
7.Explain the concept of Regularization.
Regularization is a technique used to prevent overfitting inmachine learning models. It adds a penalty term to the model’s objective
function, discouraging complex or large weights. By controlling the model’s
complexity, regularization helps in generalizing well to new, unseen data.
8.What is the difference between L1 and L2
regularization?
L1 and L2 regularization are two common forms of
regularization:
L1 regularization (Lasso) adds the absolute value of thecoefficients as a penalty term, leading to sparse solutions by encouraging some
coefficients to become zero.
L2 regularization (Ridge) adds the squared sum of the
coefficients as a penalty term, resulting in smaller but non-zero coefficients.
9.What is the difference between supervised and
unsupervised learning?
Supervised learning involves training a model on labeled
data, where the algorithm learns from input-output pairs to make predictions or
classifications. Unsupervised learning, on the other hand, deals with unlabeled
data and focuses on finding patterns, relationships, or clusters in the data
without any predefined output.
10.How does a decision tree work in machine learning?
A decision tree is a flowchart-like structure where each
internal node represents a feature, each branch represents a decision rule, and
each leaf node represents an outcome or a class label. The tree is built by
recursively splitting the data based on the most informative features until a
stopping criterion is met. During prediction, the input traverses the decision
tree, following the path based on feature values, and the corresponding outcome
is determined.
11.What is the curse of dimensionality?
The curse of dimensionality refers to the challenges faced
when working with high-dimensional data. As the number of dimensions increases,
the data becomes sparse, and the volume of the space expands exponentially.
This sparsity leads to increased computational complexity, decreased
efficiency, and a higher risk of overfitting.
12.Explain the concept of cross-validation.
Cross-validation is a technique used to assess the
performance of a machine learning model. It involves dividing the available
data into multiple subsets or folds. The model is trained on a subset of the
data and evaluated on the remaining fold. This process is repeated multiple
times, with different combinations of training and evaluation sets.Cross-validation provides a more robust estimate of a model’s performance and
helps in selecting hyperparameters and avoiding overfitting.
13.What is gradient descent, and how does it work?
Gradient descent is an optimization algorithm commonly used
in machine learning to minimize the loss function of a model. It iteratively
adjusts the model’s parameters by calculating the gradients of the loss
function with respect to each parameter. The parameters are updated in the
opposite direction of the gradient, moving towards the minimum of the lossfunction. This process continues until convergence is reached or a stopping
criterion is met.
14.What is the difference between precision and
recall?
Precision and recall are evaluation metrics used in
classification tasks:
Precision measures the proportion of correctly predicted
positive instances out of all instances predicted as positive. It focuses on
the model’s ability to avoid false positives.
Recall, also known as sensitivity or true positive rate,
measures the proportion of correctly predicted positive instances out of all
actual positive instances. It focuses on the model’s ability to avoid false
negatives.
15.Explain the concept of regularization in neural
networks.
Regularization in neural networks aims to prevent
overfitting by adding a penalty term to the loss function. It discourages
complex models by penalizing large weights or high parameter values.
Regularization techniques such as L1 and L2 regularization (also known as
weight decay) help in achieving a balance between fitting the training data
well and generalizing to unseen data.
16.What is the role of activation functions in neural
networks?
Activation functions introduce non-linearities into neural
networks, enabling them to model complex relationships between inputs and
outputs. They determine the output of a neuron based on the weighted sum of
inputs. Common activation functions include sigmoid, tanh, ReLU, and softmax,
each suited for different tasks and network architectures.
