MLA 2
what are some of the difficulties of a KNN
The k-nearest neighbor (k-NN) algorithm has several difficulties, including: 1. Curse of dimensionality: The k-NN algorithm can become less effective as the number of dimensions in the dataset increases. This is known as the curse of dimensionality, and it can lead to increased computational complexity and reduced accuracy. 2. Sensitivity to irrelevant features: The k-NN algorithm treats all features as equally important, which can lead to overfitting and reduced accuracy if there are irrelevant features in the dataset. 3. Sensitivity to outliers: The k-NN algorithm can be sensitive to outliers in the dataset, which can lead to reduced accuracy and stability. 4. Choice of k: The choice of k, the number of neighbors to consider, can have a significant impact on the performance of the k-NN algorithm. Choosing an appropriate value of k is often a matter of trial and error. 5. Imbalanced data: The k-NN algorithm can be biased towards the majority class in imbalanced datasets, leading to reduced accuracy for the minority class. 6. Large datasets: The k-NN algorithm can be computationally expensive for large datasets, as it requires calculating the distance between each pair of data points. This can make it impractical for some
The model will be trained with data in one single batch is known as? a. batch learning b. offline learning c. both a and b d. none of the above
a. Batch learning is the process of training a model with all available data in one single batch. In batch learning, the entire dataset is loaded into memory, and the model is trained on the entire dataset using a batch optimization algorithm. Batch learning is most commonly used in offline learning scenarios, where the entire dataset is available at once, and there is no need for the model to adapt to new data in real-time. In contrast, online learning involves training the model on smaller, incremental batches of data over time, allowing the model to adapt to new data and changing conditions. Therefore, option a. batch learning is the correct answer.
which of the following is not numerical functions based in its representation of machine learning? a. case-based b. Neural Network c. Linear regression d. support vector machines
a. Case-based is not a numerical function based on its representation of machine learning. Case-based reasoning is a type of machine learning that involves using past experiences or cases to solve new problems. It is a form of memory-based learning where the system stores a database of cases and uses them to make decisions or recommendations for new cases. Case-based reasoning is not a numerical function, but rather a method for solving problems based on past experiences. b. Neural network, c. Linear regression, and d. Support vector machines are all numerical functions based on their representation of machine learning. Neural networks are a type of machine learning algorithm that are modeled after the structure and function of the human brain, and are used for tasks such as image and speech recognition. Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables, and is commonly used for prediction and forecasting. Support vector machines are a type of machine learning algorithm that use a hyperplane to separate data into different classes, and are used for tasks such as classification and regression.
machine learning algorithms build a model based on sample data referred to as _? a.training data b. validation data c. modelling data d. none of the above
a. Machine learning algorithms build a model based on sample data referred to as training data. The training data is used to train the machine learning model by adjusting its parameters or weights to minimize the error between the predicted output and the actual output. The training data is a subset of the available data, and is chosen to be representative of the problem domain and to provide enough variability to enable the model to generalize to new data. Validation data is a separate subset of the data that is used to evaluate the performance of the model during training and to tune its hyperparameters. Modelling data is not a commonly used term in machine learning, and is not the correct answer to the question.
Real time decisions, Game AI, Learning tasks, Skill acquisition and Robot Navigation are applications of? a. Reinforcement learning b. supervised learning: classification c. unsupervised learning: regression d. none of the above
a. Real-time decisions, game AI, learning tasks, skill acquisition, and robot navigation are applications of reinforcement learning. Reinforcement learning is a type of machine learning that involves training an agent to make decisions in an environment by learning from feedback in the form of rewards or penalties. In reinforcement learning, the agent interacts with the environment, and its actions are guided by a policy that maximizes the expected cumulative reward over time. Real-time decisions, game AI, and robot navigation are examples of applications that require the agent to make decisions in real-time based on the current state of the environment. Skill acquisition and learning tasks involve the agent learning to perform a specific task or acquire a new skill through trial and error and feedback from the environment. b. Supervised learning: classification and c. Unsupervised learning: regression are incorrect answers for the given applications. Supervised learning: classification is a type of machine learning where the model learns to predict a categorical label or class based on input features and a labeled dataset. It is commonly used in applications such as image recognition and natural language processing. Unsupervised learning: regression is a type of machine learning where the model learns to predict a continuous output variable based on input features and an unlabeled dataset. It is commonly used in applications such as clustering and dimensionality reduction. However, these types of machine learning are not directly applicable to the given applications of real-time decisions, game AI, learning tasks, skill acquisition, and robot navigation. These applications require the agent to interact with the environment and learn from feedback, which is the domain of reinforcement learning.
which of the following is not machine learning disciplines a. physics b. information theory c. neurostatistics d. optimization control
answer is a. Physics is not a machine learning discipline. While physics can use machine learning techniques to analyze and interpret data, it is not considered a machine learning discipline in and of itself. Information theory, neurostatistics, and optimization control are all machine learning disciplines that involve the use of mathematical and statistical methods to analyze and interpret data. Information theory is concerned with the quantification and transmission of information, neurostatistics focuses on the analysis of neuroscience data, and optimization control deals with the design and optimization of control systems.
_ algorithms enable the computers to learn from data and even improve themselves without being explicitly programmed a. deep learning b. machine learning c. artificial intelligence d. none of the above
b. Machine learning algorithms enable computers to learn from data and improve themselves without being explicitly programmed. Machine learning is a subset of artificial intelligence (AI) that involves the use of algorithms and statistical models to enable machines to analyze and interpret data, identify patterns, and make predictions or decisions based on that data. Machine learning algorithms can be supervised, unsupervised, or semi-supervised, and they can be used in a variety of applications, such as image recognition, natural language processing, fraud detection, and predictive maintenance. Deep learning is a specific type of machine learning that involves the use of artificial neural networks with multiple layers, and is particularly effective for tasks such as image and speech recognition.
what is the elbow method? a. an approach to estimating 'black-box' predictions in supervised learning b.a method used to determine the optimal number of clusters in unsupervised learning for example k-mean clustering c. a method of forecasting in machine learning d. a way of assessing the fit of a machine learning algorithm
b. The elbow method is a method used to determine the optimal number of clusters in unsupervised learning, for example, k-means clustering. The method involves plotting the within-cluster sum of squares (WCSS) against the number of clusters, and selecting the number of clusters at the "elbow" point, where the change in WCSS begins to level off. The elbow point represents the point of diminishing returns, beyond which adding more clusters does not significantly improve the clustering performance. The elbow method is a popular and easy-to-use technique for selecting the appropriate number of clusters in unsupervised learning.
the problem of finding hidden structure in unlabelled data a. supervised learning b. unsupervised learning c. reinforcement learning d. none of the above
b. unsupervised learning Unsupervised learning is a type of machine learning where the model is trained on unlabelled data, meaning that the data does not have any predefined categories or classes. The goal of unsupervised learning is to find hidden patterns or structures in the data that can be used to gain insights or make predictions. This is in contrast to supervised learning, where the model is trained on labelled data, and reinforcement learning, where the model learns through trial and error based on rewards and punishments. Therefore, the problem of finding hidden structure in unlabelled data is a characteristic of unsupervised learning.
a machine learning technique that helps in detecting the outliers in data a. clustering b. classification c. anomaly detection d. all the above
c. Anomaly detection is a machine learning technique that helps in detecting the outliers in data. Anomaly detection involves identifying data points that deviate significantly from the norm or expected pattern. It is used in a variety of applications, such as fraud detection, intrusion detection, and predictive maintenance, where detecting unusual behavior is critical. Anomaly detection algorithms can be supervised or unsupervised, and they use a variety of techniques, such as distance-based methods, density-based methods, and probabilistic models, to identify anomalies in the data. Some popular anomaly detection algorithms include Isolation Forest, Local Outlier Factor, and One-Class SVM.
what is the disadvantage of decision trees? a. factor analysis b. decision trees are robust to outliers c. decision trees are prone to be overfit d. all of the above
c. Decision trees are prone to be overfit, which is a disadvantage of this algorithm. Overfitting occurs when the decision tree is too complex and fits the training data too closely, resulting in poor generalization to new data. This problem can be mitigated by using techniques such as pruning, setting a minimum number of samples required at a leaf node, and setting a maximum depth for the tree. However, decision trees can still be prone to overfitting, especially when dealing with complex or high-dimensional data. Factor analysis is not a disadvantage of decision trees, as it is a separate statistical technique used for reducing the dimensionality of data. Decision trees can be robust to outliers, as they are able to handle non-linear relationships and do not make assumptions about the distribution of the data. While decision trees can be sensitive to outliers, they are often robust to them. Factor analysis is a separate statistical technique used for reducing the dimensionality of data and is not a disadvantage of decision trees.
strategic value of data mining is _? a. cost sensitive b. work sensitive c. time sensitive d. technical sensitive
c. The strategic value of data mining is time-sensitive. Data mining can help organizations gain insights and make decisions faster by analyzing large amounts of data and identifying patterns and trends that might not be immediately apparent. By using data mining, organizations can make informed decisions quickly and respond to changing market conditions or customer needs more rapidly, giving them a competitive advantage. Time is a critical factor in business, and by using data mining, organizations can save time and resources by automating tasks and improving efficiency. While cost, work, and technical factors are also important considerations in data mining, time sensitivity is the most significant strategic value of this technique.
Identify the difficulties with the k-nearest neighbor algorithm a. curse of dimensionality b. calculate the distance of the test case from all training cases c. both a and b d. none of the above
c. both a and b
which of the following is not a data mining metric? a. return on investment b. time complexity c. space complexity d. all of the above
d. All of the above are not data mining metrics. a. Return on investment (ROI) is a financial metric that measures the profitability of an investment relative to its cost. It is not a data mining metric. b. Time complexity is a measure of the computational resources required to solve a problem as a function of the size of the input data. It is a complexity metric used in computer science and algorithms, and is not a data mining metric. c. Space complexity is a measure of the amount of memory required to solve a problem as a function of the size of the input data. It is also a complexity metric used in computer science and algorithms, and is not a data mining metric. In summary, none of the given options are data mining metrics. Data mining metrics are measures used to evaluate the performance of data mining algorithms and models, such as accuracy, precision, recall, F1 score, and AUC-ROC.
which of the following are most widely used metrics and tools to assess a classification model? a. confusion matrix b. cost sensitive accuracy c. area under the ROC curve d. all the above
d. All the above - confusion matrix, cost-sensitive accuracy, and area under the ROC curve are widely used metrics and tools to assess a classification model. The confusion matrix is a table that summarizes the performance of a classification model by comparing the predicted and actual class labels. Cost-sensitive accuracy is a metric that takes into account the cost of misclassification errors for different classes and assigns different weights to different classes. The area under the ROC curve is a measure of the trade-off between the true positive rate and false positive rate of a classification model, and is often used to compare the performance of different models. These metrics and tools are essential for evaluating the performance of a classification model and making decisions about how to improve it.
how can you handle missing or corrupted data in a dataset? a. drop rows or columns b. assign a unique category to missing values c. replace missing values with mean/median/mode d. all the above
d. All the above methods can be used to handle missing or corrupted data in a dataset, depending on the nature and extent of the missing/corrupted data and the specific requirements of the problem. a. Dropping rows or columns with missing/corrupted data is a straightforward approach, but it can lead to a loss of information and may not be feasible if the missing/corrupted data is widespread. b. Assigning a unique category to missing values is a useful strategy when the missing data is nominal or categorical in nature, and there is no meaningful way to impute the missing values. c. Replacing missing values with mean/median/mode is a common technique used when the missing data is numerical or continuous in nature. The mean/median/mode is calculated based on the available data and then used to fill in the missing values.
Among the following identify the one in which dimensionality reduction reduces a. performance b. entropy c. stochastics d. collinearity
d. Collinearity is the one in which dimensionality reduction reduces. Collinearity occurs when two or more predictor variables in a dataset are highly correlated with each other. This can lead to problems in machine learning models, such as overfitting, poor stability, and reduced interpretability. Dimensionality reduction techniques, such as principal component analysis (PCA) and singular value decomposition (SVD), can be used to reduce the dimensionality of the data and remove collinearity by transforming the original variables into a new set of uncorrelated variables. By reducing collinearity, dimensionality reduction can improve the performance of machine learning models and make them more robust and interpretable. Entropy and stochastics are not directly related to dimensionality reduction, while performance is a general term that can refer to various aspects of machine learning models.
What is the most significant phase in a genetic algorithm a. selection b. mutation c. crossover d. fitness function
d. Fitness function is the most significant phase in a genetic algorithm. The fitness function determines how well a given solution (or chromosome) performs in solving a particular problem. It assigns a fitness score to each chromosome based on how well it meets the desired criteria or objective function. The selection, crossover, and mutation phases are all important in a genetic algorithm, but they are all dependent on the fitness function to determine which chromosomes should be selected, how they should be combined, and how they should be modified. Therefore, the fitness function is often considered the most critical phase of a genetic algorithm, as it drives the search for the optimal solution.
_ is a widely used and effective machine learning algorithm based on the idea of bagging a. regression b. classification c. decision tree d. random forest
d. Random forest is a widely used and effective machine learning algorithm based on the idea of bagging. It is an ensemble learning method that combines multiple decision trees to create a more accurate and robust model. The algorithm works by randomly selecting subsets of the training data and features for each tree, and then aggregating the results of all the trees to make a final prediction. Random forest is a powerful algorithm that can handle both regression and classification tasks, and is commonly used in a variety of applications, including finance, healthcare, and marketing.
which of the following cannot be achieved using machine learning? a. accurately predicting the outcome using machine learning b. forecast the outcome variable into the future c. classify respondents into groups based on their responsive pattern d. proving causal relationships between variables
d. proving causal relationships between variables cannot be achieved using machine learning alone. Machine learning can identify correlations and patterns in data, but it cannot prove causation between variables. Establishing causation requires additional methods, such as randomized controlled experiments or quasi-experimental designs, that allow for the manipulation of variables and the control of confounding factors.
what is machine learning?
machine learning involves the autonomous acquisition of knowledge through the use of computer programs. It involves training the computer system on a large dataset, and then using that training to create a model that can be used to make predictions or decisions on new data. The system can then continue to learn and improve as more data is fed into it, allowing it to adapt to changing circumstances and make more accurate predictions over time. Machine learning is a powerful tool that has revolutionized many industries, from healthcare and finance to manufacturing and transportation.
