Course Content
1.
Accuracy Score
0 min
2 min
0
2.
Activation Function
0 min
2 min
0
3.
Algorithm
0 min
2 min
0
4.
Assignment Operator (Python)
0 min
2 min
0
5.
Artificial General Intelligence (AGI)
0 min
3 min
0
6.
Artificial Intelligence
0 min
4 min
0
7.
Artificial Narrow Intelligence (ANI)
0 min
3 min
0
8.
Artificial Neural Network (ANN)
0 min
2 min
0
9.
Backpropagation
0 min
2 min
0
10.
Bias
0 min
2 min
0
11.
Bias-Variance Tradeoff
0 min
2 min
0
12.
Big Data
0 min
2 min
0
13.
Business Analyst (BA)
0 min
2 min
0
14.
Business Analytics (BA)
0 min
2 min
0
15.
Business Intelligence (BI)
0 min
1 min
0
16.
Categorical Variable
0 min
1 min
0
17.
Clustering
0 min
2 min
0
18.
Command Line
0 min
1 min
0
19.
Computer Vision
0 min
2 min
0
20.
Continuous Variable
0 min
1 min
0
21.
Cost Function
0 min
2 min
0
22.
Cross-Validation
0 min
2 min
0
23.
Data Analysis
0 min
7 min
0
24.
Data Analyst
0 min
4 min
0
25.
Data Science
0 min
1 min
0
26.
Data Scientist
0 min
6 min
0
27.
Early Stopping
0 min
2 min
0
28.
Exploratory Data Analysis (EDA)
0 min
2 min
0
29.
False Negative
0 min
1 min
0
30.
False Positive
0 min
1 min
0
31.
Google Colaboratory
0 min
2 min
0
32.
Gradient Descent
0 min
2 min
0
33.
Hidden Layer
0 min
2 min
0
34.
Hyperparameter
0 min
2 min
0
35.
Image Recognition
0 min
2 min
0
36.
Imputation
0 min
2 min
0
37.
K-fold Cross Validation
0 min
2 min
0
38.
K-Means Clustering
0 min
2 min
0
39.
Linear Regression
0 min
2 min
0
40.
Logistic Regression
0 min
1 min
0
41.
Machine Learning Engineer (MLE)
0 min
5 min
0
42.
Mean
0 min
2 min
0
43.
Neural Network
0 min
2 min
0
44.
Notebook
0 min
3 min
0
45.
One-Hot Encoding
0 min
2 min
0
46.
Operand
0 min
1 min
0
47.
Operator (Python)
0 min
1 min
0
48.
Print Function (Python)
0 min
1 min
0
49.
Python
0 min
5 min
0
50.
Quantile
0 min
1 min
0
51.
Quartile
0 min
1 min
0
52.
Random Forest
0 min
2 min
0
53.
Recall
0 min
2 min
0
54.
Scalar
0 min
2 min
0
55.
Snake Case
0 min
1 min
0
56.
T-distribution
0 min
2 min
0
57.
T-test
0 min
2 min
0
58.
Tableau
0 min
2 min
0
59.
Target
0 min
1 min
0
60.
Tensor
0 min
2 min
0
61.
Tensor Processing Unit (TPU)
0 min
2 min
0
62.
TensorBoard
0 min
2 min
0
63.
TensorFlow
0 min
2 min
0
64.
Test Loss
0 min
2 min
0
65.
Time Series
0 min
2 min
0
66.
Time Series Data
0 min
2 min
0
67.
Test Set
0 min
2 min
0
68.
Tokenization
0 min
2 min
0
69.
Train Test Split
0 min
2 min
0
70.
Training Loss
0 min
2 min
0
71.
Training Set
0 min
2 min
0
72.
Transfer Learning
0 min
2 min
0
73.
True Negative (TN)
0 min
1 min
0
74.
True Positive (TP)
0 min
1 min
0
75.
Type I Error
0 min
2 min
0
76.
Type II Error
0 min
2 min
0
77.
Underfitting
0 min
2 min
0
78.
Undersampling
0 min
2 min
0
79.
Univariate Analysis
0 min
2 min
0
80.
Unstructured Data
0 min
2 min
0
81.
Unsupervised Learning
0 min
2 min
0
82.
Validation
0 min
2 min
0
83.
Validation Loss
0 min
1 min
0
84.
Vanishing Gradient Problem
0 min
2 min
0
85.
Validation Set
0 min
2 min
0
86.
Variable (Python)
0 min
1 min
0
87.
Variable Importances
0 min
2 min
0
88.
Variance
0 min
2 min
0
89.
Variational Autoencoder (VAE)
0 min
2 min
0
90.
Weight
0 min
1 min
0
91.
Word Embedding
0 min
2 min
0
92.
X Variable
0 min
2 min
0
93.
Y Variable
0 min
2 min
0
94.
Z-Score
0 min
1 min
0
- Save
- Run All Cells
- Clear All Output
- Runtime
- Download
- Difficulty Rating
Loading Runtime
In machine learning, the accuracy score is a metric used to evaluate the performance of a classification model. It measures the ratio of correctly predicted instances to the total number of instances in the dataset.
Accuracy = (Number of Correct Predictions / Total Number of Predictions) * 100
- Number of Correct Predictions: The count of instances where the model correctly predicts the target variable or class label.
- Total Number of Predictions: The total count of instances in the dataset.
The accuracy score provides a quick way to understand how well the model is performing in terms of overall correctness in predicting the classes. However, it might not be the best metric in certain scenarios, especially when the dataset is imbalanced (i.e., one class is significantly more prevalent than others) or when the cost of misclassifications varies between classes. In such cases, other evaluation metrics like precision, recall, F1-score, and area under the ROC curve (AUC-ROC) might be more informative.
For example, in an imbalanced dataset where the positive class occurs infrequently, a high accuracy score might be misleading if the model predominantly predicts the majority class. In such cases, precision, recall, or F1-score for each class can provide a more nuanced understanding of the model's performance.
It is a simple and intuitive metric to assess classification model performance, but it's crucial to consider other evaluation metrics and the specific characteristics of the dataset to get a comprehensive view of the model's effectiveness.