Classification involves clustering data points into groups without labels.

Classification is an unsupervised learning method that finds patterns in data.

Classification is a technique used for regression analysis in machine learning.

Classification is a supervised learning technique that categorizes data into predefined classes.

A confusion matrix represents the performance of a classification model by comparing predicted and actual classifications.

A confusion matrix indicates the number of features in a dataset.

A confusion matrix is used to visualize the training data of a model.

A confusion matrix shows the distribution of data points in a dataset.

A decision tree is a graphical representation of a random process without any data splitting.

A decision tree is a model used for classification and regression that splits data into branches based on feature values.

A decision tree is a type of neural network used for deep learning.

A decision tree is a linear model that predicts outcomes based on a single feature.

KNN predicts outcomes based on the farthest data points.

KNN requires labeled data for training only once.

KNN uses a single data point to make predictions.

KNN identifies the 'k' closest data points to make predictions based on their majority class or average value.

Data cleaning is only relevant for supervised learning.

Data cleaning only affects the speed of computation.

Data cleaning is unnecessary for model training.

Data cleaning is important because it enhances data quality, leading to better model accuracy and performance.

OpenCV, Pillow

NumPy, SciPy

TensorFlow, Keras

Pandas, Matplotlib

Supervised learning is used for clustering, while unsupervised learning is used for classification.

Supervised learning uses labeled data for training, while unsupervised learning uses unlabeled data to find patterns.

Supervised learning requires no data for training, while unsupervised learning requires labeled data.

Supervised learning can only be applied to images, while unsupervised learning can only be applied to text.

Use metrics like accuracy, precision, recall, F1 score, and confusion matrix.

Use only the training accuracy

Evaluate based on the number of features used

Focus solely on the model's runtime

Features serve as the input variables that enable a machine learning model to learn and make predictions.

Features are the final output of a machine learning model.

Features only serve to confuse the model.

Features are irrelevant to the model's performance.

Overfitting occurs when a model is too simple and cannot capture the underlying patterns in the data.

Overfitting happens when a model is trained on too little data, leading to generalization issues.

Overfitting is when a model performs equally well on both training and unseen data.

Overfitting is when a model learns the training data too well, resulting in poor performance on unseen data.

Replace missing data with the mean of the dataset

Assume all missing data is zero

Ignore the missing data completely

You can handle missing data by removing, filling, predicting, or leaving them as is.

To increase the size of the dataset for better analysis.

The purpose of normalization is to scale data to a specific range for improved algorithm performance.

To remove all missing values from the dataset.

To convert categorical data into numerical data.

Classification predicts continuous values; regression predicts categories.

Classification and regression both predict discrete outcomes.

Classification is used for time series analysis; regression is for clustering.

Classification predicts categories; regression predicts continuous values.

Testing datasets are used to create new models.

Both datasets are identical in purpose and function.

Training datasets help in model learning, while testing datasets assess model performance.

Training datasets are used for data storage only.

Hyperparameter tuning eliminates the need for feature selection.

Hyperparameter tuning reduces the amount of data needed for training.

Hyperparameter tuning is only relevant for deep learning models.

Hyperparameter tuning optimizes model performance by adjusting pre-set parameters to improve accuracy and generalization.