In the original: "Bi thing" data mining algorithmsverification of accuracy
Accuracy Validation Example 1 :based on kingdoms Database
Data preparation:
Mining Model:
In order: Naive Bayes algorithm, cluster analysis algorithm, decision tree algorithm, neural network algorithm, logistic regression algorithm, correlation algorithm
Lift Chart:
Rank to:
1. Neural Network algorithm (92.69% 0.99)
2. Logistic regression algorithm (92.39% 0.99)
3. Decision Tree Algorithm (91.19% 0.98)
4. Correlation algorithm (90.6% 0.98)
5. Clustering analysis Algorithm (89.25% 0.96)
6. Naive Bayes algorithm (87.61 0.96)
Naive Bayes algorithmClassification matrix
Description
The 538 samples of other classes had 482 predictions correctly, 32 were divided into military advisers, 24 were divided into generals, and the prediction accuracy was 89.59%.
20 samples of the adviser have 13 predictions correctly, 7 are divided into other classes, the prediction accuracy rate is 65%;
The general 112 samples had 92 predictions correctly, 16 were divided into other classes, 4 were divided into advisers, and the prediction accuracy was 82.14%.
Clustering analysis algorithmClassification matrix
Description
The 538 samples of other classes had 536 predictions correctly, 2 were divided into general categories, and the prediction accuracy was 99.63%.
20 samples of the adviser have 0 predictions correctly, 20 are divided into other classes, the prediction accuracy rate is 0%;
The general 112 samples had 62 predictions correctly, 50 were divided into other classes, and the prediction accuracy rate was 55.36%.
Decision Tree algorithmClassification matrix
Description
The 538 samples of the other classes had 538 predictions correctly, and the prediction accuracy was 100%;
20 samples of the adviser have 0 predictions correctly, 20 are divided into other classes, the prediction accuracy rate is 0%;
The general 112 samples had 73 predictions correctly, 39 were divided into other classes, and the prediction accuracy rate was 65.18%.
Neural Network algorithmclassification matrix
Description
The 538 samples of other classes had 524 predictions correctly, 5 were divided into military advisers, 9 were divided into generals, and the prediction accuracy was 97.4%.
20 samples of the adviser have 5 predictions correctly, 15 are divided into other classes, the prediction accuracy rate is 25%;
The general 112 samples had 92 predictions correctly, 20 were divided into other classes, and the prediction accuracy rate was 82.14%.
Logistic regression algorithmClassification matrix
Description
The 538 samples of other classes had 526 predictions correctly, 6 were divided into military advisers, 6 were divided into generals, and the prediction accuracy was 97.77%.
20 samples of the adviser have 5 predictions correctly, 15 are divided into other classes, the prediction accuracy rate is 25%;
The general 112 samples had 88 predictions correctly, 24 were divided into other classes, and the prediction accuracy rate was 78.57%.
Correlation algorithmclassification matrix
Description
The 538 samples of other classes had 519 predictions correctly, 19 were divided into advisers, and the prediction accuracy was 96.47%.
20 samples of the adviser have 0 predictions correctly, 20 are divided into other classes, the prediction accuracy rate is 0%;
The general 112 samples had 88 predictions correctly, 24 were divided into other classes, and the prediction accuracy rate was 78.57%.
Classification Matrixprediction of correct rate summary analysis:

Other 
Consigliere 
General 
Neural Network algorithm 
97.4% 
25% 
82.14% 
Logistic regression algorithm 
97.77% 
25% 
78.57% 
Decision Tree algorithm 
100% 
0% 
65.18% 
Correlation algorithm 
96.47% 
0% 
78.57% 
Clustering Analysis algorithm 
99.63% 
0% 
55.36% 
Naive Bayes algorithm 
89.59% 
45g 
82.14% 
It can be seen that the naive Bayes algorithm has the highest accuracy in predicting the status of a strategist, reaching 65%, decision Tree Algorithm, association algorithm, clustering algorithm 0%, neural network algorithm and logistic regression algorithm 25%;
The decision tree algorithm has the highest accuracy in predicting other identities, reaching 100%.
Neural network algorithm and Naive Bayes algorithm are used to predict the accuracy of general identity, and reach 82.14%.
Accuracy Verification example 2:based on stock data
Data preparation:
The mining model is in turn:
Stockclustering Clustering Analysis algorithm
Strockdecisiontrees Decision Tree Algorithm
Stockneuralnetwork Neural Network algorithm
Stocklogistic Logistic regression algorithm
Lift Chart:
rank to:
1. Logistic regression algorithm (49.73% 0.52)
2. Neural network algorithm (49.63% 0.53)
3. Clustering analysis Algorithm (48.13% 0.51)
4. Decision Tree Algorithm (47.28% 0.50)
Cluster analysis algorithmClassification matrix:
Description
The flat 114 samples had 0 predictions correctly, 91 were divided into fall, 23 were divided into up, the prediction accuracy was 0%;
The 443 samples of the fall have 340 predictions correctly, 103 errors are up, and the forecast accuracy rate is 76.75%;
The 380up sample has 111 predictions correctly, 269 errors fall, and the forecast accuracy rate is 29.21%.
Decision Tree algorithmClassification matrix:
Description
The flat 114 samples have 0 predictions correctly, 114 mistakes fall, and the forecast accuracy rate is 0%;
443 of the 443 samples dropped were correctly predicted and the correct rate was 100%;
The 380up sample has 0 predictions correctly, 380 errors fall, and the forecast accuracy rate is 0%.
Neural Network algorithmClassification matrix:
Description
The flat 114 samples had 0 predictions correctly, 60 were divided into fall, 54 were divided into up, the prediction accuracy was 0%;
The 443 samples of the fall have 277 predictions correctly, 166 errors are up, and the forecast accuracy rate is 62.53%;
The 380up sample has 188 predictions correctly, 192 errors fall, and the forecast accuracy rate is 49.47%.
Logistic regression algorithmClassification matrix:
Description
The flat 114 samples had 0 predictions correctly, 89 were divided into fall, 25 were divided into up, the prediction accuracy was 0%;
The 443 samples of the fall have 380 predictions correctly, 63 errors are up, and the forecast accuracy rate is 85.78%;
The 380up sample has 86 predictions correctly, 294 errors fall, and the forecast accuracy rate is 22.63%.
Classification Matrixprediction of correct rate summary analysis:

Flat 
Fell 
Rose 
Logistic regression algorithm 
0% 
85.78% 
22.63% 
Neural Network algorithm 
0% 
62.53% 
49.47% 
Clustering Analysis algorithm 
0% 
76.75% 
29.21% 
Decision Tree algorithm 
0% 
100% 
0% 
"Bi thing" data mining algorithmsverification of accuracy