Welcome to the thrilling world of Tic-Tac-Toe, where Xs and Os dance across the grid in a quest for victory. We’ve all experienced the rush of adrenaline that comes with making that winning move or desperately blocking an opponent’s strategic advance. We’ve all tasted the thrill of victory and the sting of defeat. But what if there’s more to the endgame than meets the eye?
In this journey through the world of Tic-Tac-Toe, we’re about to unveil a fascinating exploration into the possible configurations of the game. In this exploration, we’ll delve into the strategic intricacies of the endgame, leveraging a binary classification model to predict optimal moves and decode the patterns that lead to triumphant outcomes. Imagine unlocking the mysteries of each move, predicting your opponent’s next step, and gaining an edge that could turn the tide in your favor.
Get ready to dive into the heart of this childhood favorite as we delve into the realm of binary classification models, shedding light on the strategic nuances that make Tic-Tac-Toe an endless source of excitement and strategic depth. Get set to rethink your approach to this timeless game as we embark on a quest to decode its secrets!
LET’S TALK DATA
The dataset used for building the binary classification model has 958 observations and 9 main feature along with 1 dummy feature(constant). It is a multivariate categorical dataset without any missing values. The variables are:
- top-left-square: {x,o,b}
- top-middle-square: {x,o,b}
- top-right-square: {x,o,b}
- middle-left-square: {x,o,b}
- middle-middle-square: {x,o,b}
- middle-right-square: {x,o,b}
- bottom-left-square: {x,o,b}
- bottom-middle-square: {x,o,b}
- bottom-right-square: {x,o,b}
- Class: {positive,negative}
- dummy (constant variable)
(x=player x has taken, o=player o has taken, b=blank)
This database encodes the complete set of possible board configurations
at the end of tic-tac-toe games, where “x” is assumed to have played
first. The target concept is “win for x” (i.e., true when “x” has one
of 8 possible ways to create a “three-in-a-row”). The dataset did not need any pre-processing and was used as such to build the binary classification model which predicts if the victory is for x(positive) or not(negative).
NO-CODE AI MODEL
Using the Google sheets add-on PredictEasy a classification model was built. In order to know more about how to use the tool please refer to my previous blog posts. To interpret the visualizations that the tool provides click here.
The tool helped me build a classification model with 99% accuracy in just a few minutes. It also gave an interface in which I can feed the inputs of the x player and predict the possibility of win for x. Let’s take a look into the results.
The predictive model achieved an accuracy of 99%, indicating that it is able to correctly classify the target class with a high level of accuracy.
The precision score of 99% suggests that the model has a low rate of false positives, meaning that it is good at identifying the positive class correctly.
The recall score of 99% indicates that the model has a low rate of false negatives, meaning that it is good at capturing instances of the positive class.
The F1 score of 99% is a balanced measure of precision and recall, indicating that the model performs well in terms of both metrics.
The ROC curve and Confusion Matrix supports the model’s accuracy.
The XAI and Feature Rank plot suggests that all the variables are almost equally important except the dummy variable which has no importance. The plots also shows that none of the features can be excluded and all of them are equally important for the prediction. The feature scores provide insights into the importance of different features in predicting the target class. The most important feature for the model is “middle_middle_square”, followed by “bottom_right_square” and “bottom_left_square”. The high importance of “middle_middle_square” suggests that this feature has a strong influence on the prediction of the target class. The other top features, such as “bottom_right_square” and “bottom_left_square”, also contribute significantly to the model’s predictive power.
Further investigation into the characteristics of the “middle_middle_square” feature could provide insights into why it is the most important feature. Understanding the relationship between the top features and the target class could help identify patterns or correlations that contribute to the prediction.
The real time interface example shown above clearly shows how the model works with its accurate prediction. The input data will look as shown below in real time.
This is a clear win for x which the model also states that with 100%. Similarly, we can try out the real time interface by giving the various inputs accordingly and finding the probability of win for x.
Based on the high accuracy, precision, recall, and F1 scores, the predictive model appears to be performing well. The identified top features can be used to gain a better understanding of the factors influencing the target class. Further analysis and experimentation can be conducted to validate the importance of the top features and explore potential improvements to the model. It is recommended to focus on the top features and investigate their relationships with the target class to gain deeper insights and potentially enhance the model’s performance.
In the intricate tapestry of Tic-Tac-Toe’s endgame, our journey has revealed the power of a binary classification model in enhancing strategic decision-making for player X. Unraveling the complexities of each move, we’ve gained valuable insights into the subtle dance of Xs and Os on the board. As we conclude this exploration, it’s clear that the fusion of human intuition and machine-assisted analysis opens new doors to understanding and mastering this timeless game. Armed with a deeper appreciation for the nuances of the endgame, players can approach Tic-Tac-Toe with renewed insight, transforming each match into a strategic endeavor that transcends the simplicity of three-in-a-row.